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• What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

The aim is to gain as thorough an understanding as possible of the case and its context.

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In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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What Is a Case Study?

Weighing the pros and cons of this method of research

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Cara Lustik is a fact-checker and copywriter.

Verywell / Colleen Tighe

• Pros and Cons

What Types of Case Studies Are Out There?

Where do you find data for a case study, how do i write a psychology case study.

A case study is an in-depth study of one person, group, or event. In a case study, nearly every aspect of the subject's life and history is analyzed to seek patterns and causes of behavior. Case studies can be used in many different fields, including psychology, medicine, education, anthropology, political science, and social work.

The point of a case study is to learn as much as possible about an individual or group so that the information can be generalized to many others. Unfortunately, case studies tend to be highly subjective, and it is sometimes difficult to generalize results to a larger population.

While case studies focus on a single individual or group, they follow a format similar to other types of psychology writing. If you are writing a case study, we got you—here are some rules of APA format to reference.  

At a Glance

A case study, or an in-depth study of a person, group, or event, can be a useful research tool when used wisely. In many cases, case studies are best used in situations where it would be difficult or impossible for you to conduct an experiment. They are helpful for looking at unique situations and allow researchers to gather a lot of˜ information about a specific individual or group of people. However, it's important to be cautious of any bias we draw from them as they are highly subjective.

What Are the Benefits and Limitations of Case Studies?

A case study can have its strengths and weaknesses. Researchers must consider these pros and cons before deciding if this type of study is appropriate for their needs.

One of the greatest advantages of a case study is that it allows researchers to investigate things that are often difficult or impossible to replicate in a lab. Some other benefits of a case study:

• Allows researchers to capture information on the 'how,' 'what,' and 'why,' of something that's implemented
• Gives researchers the chance to collect information on why one strategy might be chosen over another
• Permits researchers to develop hypotheses that can be explored in experimental research

On the other hand, a case study can have some drawbacks:

• It cannot necessarily be generalized to the larger population
• Cannot demonstrate cause and effect
• It may not be scientifically rigorous
• It can lead to bias

Researchers may choose to perform a case study if they want to explore a unique or recently discovered phenomenon. Through their insights, researchers develop additional ideas and study questions that might be explored in future studies.

It's important to remember that the insights from case studies cannot be used to determine cause-and-effect relationships between variables. However, case studies may be used to develop hypotheses that can then be addressed in experimental research.

Case Study Examples

There have been a number of notable case studies in the history of psychology. Much of  Freud's work and theories were developed through individual case studies. Some great examples of case studies in psychology include:

• Anna O : Anna O. was a pseudonym of a woman named Bertha Pappenheim, a patient of a physician named Josef Breuer. While she was never a patient of Freud's, Freud and Breuer discussed her case extensively. The woman was experiencing symptoms of a condition that was then known as hysteria and found that talking about her problems helped relieve her symptoms. Her case played an important part in the development of talk therapy as an approach to mental health treatment.
• Phineas Gage : Phineas Gage was a railroad employee who experienced a terrible accident in which an explosion sent a metal rod through his skull, damaging important portions of his brain. Gage recovered from his accident but was left with serious changes in both personality and behavior.
• Genie : Genie was a young girl subjected to horrific abuse and isolation. The case study of Genie allowed researchers to study whether language learning was possible, even after missing critical periods for language development. Her case also served as an example of how scientific research may interfere with treatment and lead to further abuse of vulnerable individuals.

Such cases demonstrate how case research can be used to study things that researchers could not replicate in experimental settings. In Genie's case, her horrific abuse denied her the opportunity to learn a language at critical points in her development.

This is clearly not something researchers could ethically replicate, but conducting a case study on Genie allowed researchers to study phenomena that are otherwise impossible to reproduce.

There are a few different types of case studies that psychologists and other researchers might use:

• Collective case studies : These involve studying a group of individuals. Researchers might study a group of people in a certain setting or look at an entire community. For example, psychologists might explore how access to resources in a community has affected the collective mental well-being of those who live there.
• Descriptive case studies : These involve starting with a descriptive theory. The subjects are then observed, and the information gathered is compared to the pre-existing theory.
• Explanatory case studies : These   are often used to do causal investigations. In other words, researchers are interested in looking at factors that may have caused certain things to occur.
• Exploratory case studies : These are sometimes used as a prelude to further, more in-depth research. This allows researchers to gather more information before developing their research questions and hypotheses .
• Instrumental case studies : These occur when the individual or group allows researchers to understand more than what is initially obvious to observers.
• Intrinsic case studies : This type of case study is when the researcher has a personal interest in the case. Jean Piaget's observations of his own children are good examples of how an intrinsic case study can contribute to the development of a psychological theory.

The three main case study types often used are intrinsic, instrumental, and collective. Intrinsic case studies are useful for learning about unique cases. Instrumental case studies help look at an individual to learn more about a broader issue. A collective case study can be useful for looking at several cases simultaneously.

The type of case study that psychology researchers use depends on the unique characteristics of the situation and the case itself.

There are a number of different sources and methods that researchers can use to gather information about an individual or group. Six major sources that have been identified by researchers are:

• Archival records : Census records, survey records, and name lists are examples of archival records.
• Direct observation : This strategy involves observing the subject, often in a natural setting . While an individual observer is sometimes used, it is more common to utilize a group of observers.
• Documents : Letters, newspaper articles, administrative records, etc., are the types of documents often used as sources.
• Interviews : Interviews are one of the most important methods for gathering information in case studies. An interview can involve structured survey questions or more open-ended questions.
• Participant observation : When the researcher serves as a participant in events and observes the actions and outcomes, it is called participant observation.
• Physical artifacts : Tools, objects, instruments, and other artifacts are often observed during a direct observation of the subject.

If you have been directed to write a case study for a psychology course, be sure to check with your instructor for any specific guidelines you need to follow. If you are writing your case study for a professional publication, check with the publisher for their specific guidelines for submitting a case study.

Here is a general outline of what should be included in a case study.

Section 1: A Case History

This section will have the following structure and content:

Background information : The first section of your paper will present your client's background. Include factors such as age, gender, work, health status, family mental health history, family and social relationships, drug and alcohol history, life difficulties, goals, and coping skills and weaknesses.

Description of the presenting problem : In the next section of your case study, you will describe the problem or symptoms that the client presented with.

Describe any physical, emotional, or sensory symptoms reported by the client. Thoughts, feelings, and perceptions related to the symptoms should also be noted. Any screening or diagnostic assessments that are used should also be described in detail and all scores reported.

Your diagnosis : Provide your diagnosis and give the appropriate Diagnostic and Statistical Manual code. Explain how you reached your diagnosis, how the client's symptoms fit the diagnostic criteria for the disorder(s), or any possible difficulties in reaching a diagnosis.

Section 2: Treatment Plan

This portion of the paper will address the chosen treatment for the condition. This might also include the theoretical basis for the chosen treatment or any other evidence that might exist to support why this approach was chosen.

• Cognitive behavioral approach : Explain how a cognitive behavioral therapist would approach treatment. Offer background information on cognitive behavioral therapy and describe the treatment sessions, client response, and outcome of this type of treatment. Make note of any difficulties or successes encountered by your client during treatment.
• Humanistic approach : Describe a humanistic approach that could be used to treat your client, such as client-centered therapy . Provide information on the type of treatment you chose, the client's reaction to the treatment, and the end result of this approach. Explain why the treatment was successful or unsuccessful.
• Psychoanalytic approach : Describe how a psychoanalytic therapist would view the client's problem. Provide some background on the psychoanalytic approach and cite relevant references. Explain how psychoanalytic therapy would be used to treat the client, how the client would respond to therapy, and the effectiveness of this treatment approach.
• Pharmacological approach : If treatment primarily involves the use of medications, explain which medications were used and why. Provide background on the effectiveness of these medications and how monotherapy may compare with an approach that combines medications with therapy or other treatments.

This section of a case study should also include information about the treatment goals, process, and outcomes.

When you are writing a case study, you should also include a section where you discuss the case study itself, including the strengths and limitiations of the study. You should note how the findings of your case study might support previous research. 

In your discussion section, you should also describe some of the implications of your case study. What ideas or findings might require further exploration? How might researchers go about exploring some of these questions in additional studies?

Need More Tips?

Here are a few additional pointers to keep in mind when formatting your case study:

• Never refer to the subject of your case study as "the client." Instead, use their name or a pseudonym.
• Read examples of case studies to gain an idea about the style and format.
• Remember to use APA format when citing references .

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach .  BMC Med Res Methodol . 2011;11:100.

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach . BMC Med Res Methodol . 2011 Jun 27;11:100. doi:10.1186/1471-2288-11-100

Gagnon, Yves-Chantal.  The Case Study as Research Method: A Practical Handbook . Canada, Chicago Review Press Incorporated DBA Independent Pub Group, 2010.

Yin, Robert K. Case Study Research and Applications: Design and Methods . United States, SAGE Publications, 2017.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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The Case Study Approach

“The person who can combine frames of reference and draw connections between ostensibly unrelated points of view is likely to be the one who makes the creative breakthrough.” —Denise Shekerjian

In the previous section, our framework describing the interaction of multiple competing messages provided a useful way to describe how risk communicators should create convergence and understanding with their audiences in pre-crisis, crisis, and post-crisis situations. In addition, the identification of best practices offers a way to identify why particular risk messages may have more influence than others on how audiences respond. Adding to the complexity of the situation for risk communicators are multiple publics who may not share the same understanding or willingness to respond to the messages due to how the risk or potential crisis may affect them in what we described as spheres of ethnocentricity.

In the complex communication context of risk communication, one research methodology is particularly appropriate, due to its capacity to explore, describe, or explain the dynamics of the situation. The case study approach to research in the social sciences is a fitting method for identifying the interaction between individuals, messages, and context. Yin ) summarizes, “The case study method allows investigators to retain the holistic and meaningful characteristics of real-life events” (p. 2). The case study approach works well to identify best practices for risk communication because individual situations are defined or isolated, relevant data are collected about the situation, and the findings are presented in such a way that a more complete understanding is reached regarding how messages shape perceptions and serve to prompt particular responses from those hearing the messages.

We consider the case study method as both an approach to research and a choice of what to study (Patton, 2002 ). Therefore, in the construction of the case studies presented in this book, a consistent methodological approach was followed. In order to establish common areas of analysis within the research design, we focused on risk situations involving the unintentional or intentional contamination or compromise of the food system. A conceptual framework based upon the best practices explained in 10.1007/978-0-387-79727-4_2 and a chronological exposition of the pre-crisis, crisis, and post-crisis messages created consistency as we drew implications about what happened, how it happened, and why. Collectively, the cases allowed us to generalize about the best practices as a whole.

Individually, the choice of cases provided opportunities to demonstrate various aspects of the best practices. Each of the forthcoming cases includes particular situations and context-sensitive information whereby the best practices for risk communication could be identified and studied. In some cases, preemptive communication strategies designed to promote compliant behavior are found. In others, the exposition of the crisis revealed how risks were not anticipated or communicated effectively to the public. In fact, as demonstrated by how a crisis actually unfolded, the evidence suggests that those managing the crisis were not always mindfully considering the competing arguments. Rather than seeking congruence, reliance on economic or social models enabled decision-makers to simplify the risk situation at a time when complexity should have been acknowledged. In such situations, the value of the best practices may not have been recognized until after the crisis had passed.

Justification for the Case Study Approach

We selected the case study approach as a way to illustrate the interactive process involved in the convergence of ris(2003k messages for several reasons. Case studies have been used frequently by scholars and practitioners in public health, agriculture, education, psychology, and the social sciences as a legitimate methodological approach to research (Rogers, 2003 ; Tuschman & Anderson, 1997 ). In addition, they provide a method to investigate a contemporary event involving risk within a real life context; and they contribute to enhanced knowledge of complex social phenomena.

Legitimacy as a Methodological Approach

The case study approach has been used to study many different situations involving individual, group, organizational, social, political, and related phenomena (Yin, 2003). Throughout his treatise on diffusion theory, Rogers ( 2003 ) offers cases to illustrate the following: social systems (Iowa hybrid corn case), pro-innovation bias (Egyptian villages pure drinking water case), socioeconomic status (California hard tomatoes case), the reinvention process (horse culture among the Plains Indians case), attributions of innovations (photovoltaics, cellular telephones case), adopter types (Old Order Amish case), opinion leadership (Alpha Pups in the viral marketing of an electronics game case), diffusion networks (London Cholera epidemic case), change agents (Baltimore needle-exchange project case), stages in the innovation process (Santa Monica freeway diamond lane experiment case), and the consequences of innovations (steel axes for stone-age Aborigines case), to name but a few.

In their collection of readings on managing strategic innovation and change, Tuschman and Anderson ( 1997 ) offer numerous case studies involving technology cycles, design changes, power dynamics in organizations, managing research and development, product development, cross-functional linkages, and leadership styles. Similarly, risk and crisis scholars have used case studies to illustrate best practices and organizational learning.

Sellnow and Littlefield ( 2005 ) use case studies describing both accidental and intentional contamination to demonstrate lessons learned about protecting America's food supply. Three cases focused on particular companies and their experiences managing a crisis: Schwan's demonstration of social responsibility in response to a Salmonella contamination crisis, Chi-Chi's inability to survive a Hepatitis A outbreak despite apologia, and Jack in the Box restaurants' organizational learning following an E. coli outbreak in Seattle, Washington. A case involving interagency coordination and the tainted strawberries in the National School Lunch Program revealed how various stakeholders affect crisis planning efforts. Two cases of potentially intentional contamination—one by Monsanto, a major producer of genetically engineered wheat and the other by the Boghwan Shree Rajneesh cult in an Oregon community—explored the effect of public opinion and outrage.

In their work, Ulmer et al. ( 2007 ) provide case studies revealing lessons learned about managing uncertainty, effective communication, and demonstrating leadership. They focused on four areas: industrial disasters (Exxon and the Valdez oil tanker, and the fires at Malden Mills and Cole Hardwoods), food borne illness (Jack in the Box's E. coli O157:H7, Hepatitis A at a Chi-Chi's restaurant, and the Schwan's Salmonella crisis), terrorism (the case of 9/11, the Oklahoma City bombing, and the CDC's handling of the SARS outbreak), and natural disasters (the 1997 Red River Valley floods, the Tsunami and the Red Cross, and the 2003 San Diego County fires).

Exploring risk and crisis situations in public health, Seeger et al. ( 2008 ) categorize case studies focusing on bioterrorism, food borne illness, infectious disease outbreaks, and crisis prevention and responses. Cases of bioterrorism focused on lessons learned from the 2001 Anthrax crisis through the U.S. Postal System; the threat of agro-terrorism in high reliability organizations and organizational responses to the Chi-Chi's Hepatitis A outbreak provided cases demonstrating the risk of food-borne illness and the need for crisis prevention; and the strategies used by communities, nations, and the world when dealing with the risk of West Nile Virus, SARS, Encephalitis, HIV and AIDS provided cases where infectious disease outbreaks required effective risk and crisis responses. These collections and others similarly have found value in studying particular examples of an identified phenomenon for the benefit of understanding more about what, how, and why something happened.

Multiple Sources of Information

One of the reasons supporting the legitimacy of the case study approach is its use of multiple sources of information to establish claims about a particular situation.

Multiple sources may include textual materials, on-line websites and resources, interviews, media accounts, and personal observations. Due to the nature of the case study approach, choices must be made about the kinds of information to be utilized. Accessibility often dictates the kinds of information to be included, in which case the researchers must continually cross reference to be sure that the most accurate depiction of the situation is conveyed.

For the case studies included within this volume, text-based materials provided the majority of the information consulted (Fig. 4.1 ). Information drawn from national newspapers (e.g., New York Times and The Wall Street Journal ), regional newspapers (e.g., The Boston Globe) or—as in the New Zealand foot and mouth hoax case—international outlets (e.g., Financial Times and The Dominion Post ) provided contextual material enabling the researcher to establish the time frame and variables at work in each case. Websites and on-line materials, such as those offered by governmental and industrial groups provided insight from the perspective of those in positions to respond to the risk or crisis situation. A number of groups were accessed through such websites, including the U.S. Department of Health and Human Services, the Department for the Environment, Food, and Rural Affairs, Odwalla, Inc., KidSource Online, and ConAgra Foods. Interviews were conducted with individuals holding positions of responsibility, enhancing the researcher's understanding of the dynamics of the situation in New Zealand.

Multiple sources of information used in case studies

When interviewing was impossible, official comments from key decision-makers were drawn from the available textual sources. Together, these multiple sources enabled the observer to engage in triangulation, a process where more than one source of information is used when drawing inferences or conclusions about a given situation. Stake ( 2000 ) argues that triangulation was valuable not only to clarify meaning, but also to identify “different ways the phenomenon is being seen” (p. 444).

Need for Theoretical Framework

In addition to the need for multiple sources of data to understand the complexity of a risk or crisis situation, another reason researchers use the case study approach stems from the way theoretical propositions may be used to guide data collection and analysis. Case study researchers can set the parameters for what will be included within the analysis. As such, the introduction of a theoretical framework provides an overlay for the data that the researcher may use as a way to explore, describe, or explain what happened. In the selected cases included in this volume, the researchers utilized existing theoretical perspectives about risk and crisis drawn from the professional journals of the field, including Journal of Applied Communication Research, Management Communication Quarterly, Journal of Epidemiol Community Health , and The New England Journal of Medicine . The existing theoretical framework provided a backdrop for considering each case.

Specifically for this volume, best practices for risk communication were used as a theoretical framework (Fig. 4.2 ). As already explained in 10.1007/978-0-387-79727-4_2, these best practices are theory driven and stem from the work previously done through a collaboration of risk and crisis communicators who introduced the ten best practices for crisis communication through the National Center for Food Protection and Defense (Seeger, 2006 ). Each of the case studies used these best practices to help to reveal problems faced by risk and crisis communicators, as well as to identify the strategies used as individuals, organizations, and communities worked to move through the crisis to recovery and in some cases, renewal.

Best practices of risk communication

Utility for Investigation into Contemporary Events

In addition to the case study method being multidimensional, researchers value the approach because it provides an empirical way to investigate a contemporary phenomenon within a real life context. There are differences between the case study approach and studies utilizing a more structured research methodology. For example, in an experimental setting, variables may be controlled or accounted for as particular actions are taken to affect the outcome. In this closed environment, researchers can make generalizations based upon the sophistication of their design.

However, situations where risk messages are communicated through the media and events are reported and presented as they unfold, researchers have less control over how competing risk message are transmitted and received by diverse groups within the public. The range of variables that cannot be controlled or manipulated further complicates the coverage of contemporary events outside of the laboratory. For example, 10.1007/978-0-387-79727-4_8 examines the case of in the tainted Odwalla juice, the Cryp-tosporidium outbreak case, or in the case of finding Salmonella in ConAgra Foods pot pies, human error could not have been predicted with certainty. In disasters like Hurricane Katrina, elements of nature could not be controlled. They happened. In the New Zealand hoax case, the potential threat of a terrorist's intentional foot and mouth disease could not have been precluded. The realization that a host of variables are interacting in a real-world setting affords the scholar a unique opportunity to explore, describe, and explain events as they occur.

The opportunity to examine what transpired in a particular crisis situation is unique to the case study approach. Due to the dynamic, chaotic nature of crisis events that are not always represented in cause-to-effect relationships, the case study approach enabled us to examine and understand situations in ways that might not have been foreseen prior to the start of our investigation. While not statistically generalizable, after examining several cases, the identification of the presence or absence of the best practices provides researchers with the arguments needed to find consistency about the situation that may have applicability to other similar risk situations. By using the case study framework to separate the pre-crisis from the crisis, observers may note events leading up to the crisis, factors that may have contributed to the way the risks were presented, and what happened (or should have happened) as a result of the way these messages were processed and acted upon.

Enhancement of Knowledge About Complex Phenomena

Within any given situation involving risk, there are many variables of interest, including the processes at work within the dynamic of the situation; the changes that occur due to the introduction of particular risk messages; relations between various stakeholders during the pre-crisis, crisis, or post-crisis situations; and the learning that results following the response to a crisis situation. These variables involving individuals, groups, organizations, or social entities represent the multidimensionality of the phenomena involved in risk communication.

In addition, the varied nature of questions posed by researchers and practitioners pertaining to risk situations further demonstrates how the complexity of a case can be studied using this approach. Case studies seeking to know what happened in a particular context rely on “what” questions. What happened in a particular situation causing a response? When researchers seek answers to “how” questions, they want descriptions. How did an entity communicate risk messages? Researchers seeking explanations regarding the particular motivations of communicators in a situation rely on “why” questions. Why were company spokespeople compelled to communicate particular messages to the public about a risk situation? In contrast to quantitative and qualitative methodologies where researchers tend to focus on one dimension or variable, the case study approach enables the researcher to use all of these questions. Questions like these are used throughout the cases to reveal the multidimensionality of the risk and crisis events.

Establishing a Framework for Case Studies

Identifying a framework for case studies is essential if comparisons are to be made. Stake ( 2000 ) suggests the following items as essential in the creation of a case study:

The nature of the case; the case's historical background; the physical setting; other contexts (e.g., economic, political, legal, and aesthetic); other cases through which this case is recognized; and those informants through whom the case can be known. (pp. 438– 439)

Thus, to provide clarity, we determined that each case study should be written to include common elements providing comparable information for the reader to consider. In the following case studies, we provide:

• An introduction and overview of the case.
• Evidence and application of the best practices for risk communication within the case.
• Lessons learned and implications drawn from the use of best practices for risk communication.

From a research perspective, with these elements as constants, individual authors were able to gather data appropriate to each case and uniformly present their findings. In addition, similar textual materials were used in each of the studies, providing the reader with comparable information to consider.

Five Cases of Risk Communication

Stake ( 2000 ) argues that, “perhaps the most unique aspect of the case study is the selection of cases to study” (p. 446). With this in mind, we selected crisis situations where the presence or absence of best practices for risk communication could be identified, providing the readers with insight into how risk communication may or could have been used to affect the behavior of various stakeholders prior to the onset of a crisis situation. Each case study is unique in the risks posed, as well as how the communication agent sought to affect compliant behavior from the various stakeholders receiving the risk messages.

In the case of the Cryptosporidium crisis, the risks associated with water quality in a major metropolitan area and a community's response to a water quality crisis are examined. The risks associated with inadequate planning and the events related to the Hurricane Katrina disaster reveal how different levels of the government responded to a natural disaster. A government's use of interacting arguments revealed a paradox between appearing to accept the risk of foot and mouth disease and dismissing its likelihood on a New Zealand island. The Odwalla case study focuses on the risks associated with their trademark apple juice and how it struggled to renew itself within the health food industry. Finally, the ConAgra Foods Salmonella case study features the complexities of addressing multiple audiences during a major recall event involving pot pies.

“ Cryptosporidium: Unanticipated Risk Factors ,” provides the example of a community organization that experienced a crisis because it did not respond in time to government warnings calling for stronger guidelines for guarding municipal water against Cryptosporidium invasions. At the time of the crisis, Milwaukee had no water monitoring systems in place and the outbreak served as a wake-up call by exposing weaknesses in the public health system and pointing out the bioterrorism risks. Throughout the crisis, community leaders failed to be open, honest, and timely with the information they provided to the public. They failed to be mindful of public concerns expressed prior to the cryptosporidium outbreak. In addition, they did not collaborate or coordinate across agencies, exacerbating the crisis. Milwaukee was unprepared but learned from the event, established a plan should such a crisis occur in the future, and now has one of the safest water treatment systems in the country.

“Hurricane Katrina: Risk Communication in Response to a Natural Disaster,” examines how local leaders failed to create an adequate crisis plan, despite having knowledge of the damage that would occur if a hurricane of Katrina's magnitude struck New Orleans. While local crisis managers had a plan, its usefulness was mitigated by the length and format of the document. Once the hurricane struck, New Orleans crisis managers faced the difficult challenge of collaborating and coordinating resource distribution to affected residents. Another difficulty was getting information to the stakeholders. In the pre-crisis and crisis stages, the media were often ahead of local officials in presenting information to residents. This compromised the local officials' credibility and accountability. Clearly, lack of pre-event planning, the absence of collaboration and coordination, and the need for honest, candid, open, and accountable communication are key reasons why local crisis managers were unable to plan for, manage, and move past what was a devastating event for New Orleans and the surrounding region.

“New Zealand Beef Industry: Risk Communication in Response to a Terrorist Hoax” expands knowledge of risk communication by introducing how hoaxes and terrorist threats complicate our understanding of risk situations. In New Zealand, after receiving a threat claiming a deliberate release of the foot and mouth disease virus on Waiheke Island, the government had to provide intersecting messages demonstrating their capacity to manage the crisis situation. In essence, they claimed to be treating the situation as a potential crisis, while at the same time indicating their belief that the threat was a hoax. Managing crisis uncertainty became the focus for local leaders as they presented messages minimizing the risk as a hoax while acknowledging their treatment of the message as a viable threat to the security of the cattle and the New Zealand economy. The pre-crisis partnerships established between crisis managers and the various stakeholders proved valuable as the agencies worked together to disseminate information and communicate with the local citizens, as well as New Zealand's international partners. While a crisis plan was in place, and had been tested, there were some initial concerns raised by the local public that were ultimately mitigated due to open communication, as well as an attitude of compassion and empathy demonstrated by crisis spokespeople. While the hoax never developed into a crisis, providing messages of self-efficacy about checking for symptoms became an effective way to garner public confidence.

How a company managed to survive the challenge of an E. coli outbreak associated with one of its juice products is the subject of the chapter, “Odwalla: The Long Term Implications of Risk Communication.” Despite the potential risks associated with the continued consumption product, the public stood by Odwalla and its actions during and after the crisis. Odwalla met the needs of the media and remained accessible by holding press conferences, continually updating a website, instituting a hotline, and maintaining open communication with consumers and the press. The company delivered messages of self-efficacy and offered multiple ways for consumers to remain safe. In addition, company leaders apologized publicly, acknowledged the tragedy of the situation, paid medical bills for victims, and acknowledged the impact of the crisis on the image of the company. Following the crisis, Odwalla created an advisory council that ultimately recommended a new pasteurization process, breaking new ground in the industry. The use of some of the best practices enabled Odwalla to embrace a crisis, use it as an opportunity to become an industry leader, initiate industry wide change, and to encourage organizational renewal.

“ConAgra: Audience Complexity in Risk Communication” focuses on the need for organizations to consider multiple audiences when issuing risk messages. In the process of what appeared to be a demonstration of more concerned about their bottom line than with the safety of their customers, ConAgra initially shifted the blame for the outbreak to consumers for not cooking the pot pies properly. In addition, ConAgra made overly-assuring statements to the public about which products were affected by Salmonella (chicken and turkey), and which were not (beef). The assumptions made by ConAgra Foods about the literacy levels, economic status, access to media, proximity to outbreak, and cultural group identities of those receiving the risk messages also complicated their communication with stakeholders. In this case, once Salmonella was linked to ConAgra Foods' pot pies, the company issued a recall of all brands associated with their product. While additional information about the ConAgra Foods recall has yet to emerge, the case points to the need for greater attention by company spokespeople to the best practices of risk communication in order to preserve a positive reputation with the public.

This chapter introduced the case study method as a viable way to study risk communication in crisis situations. Our reasons for choosing the case study approach include its utility for exploring situations from multiple points of view, its usefulness when investigating contemporary events, and its ability to provide enhanced knowledge about complex phenomena. The best practices of risk communication, based on the best practices of crisis communication (Seeger, 2006 ), provide the theoretical framework for the case studies included in this book.

The framework we used for the case studies includes an introduction and overview to the case, a timeline of events, evidence and application of the best practices, and lessons learned. Five cases were introduced: the Milwaukee Cryp-tosporidium crisis, the Hurricane Katrina crisis, the New Zealand foot and mouth disease hoax crisis, the Odwalla juice crisis, and the ConAgra Foods Salmonella crisis. In each case, the best practices of risk communication provide insight into what occurred, or failed to occur, and the implications that followed in each crisis situation.

The five case studies provide insight into the best practices of risk communication. In all of the cases, risk communicators should have acknowledged competing arguments in the construction of risk messages. For example, the Cryptosporid-ium case demonstrates the need to infuse risk communication into policy making. By accepting that current practices would take care of the problem, local leaders allowed the crisis to develop. In the Hurricane Katrina case, the dynamic state of affairs required communicators to continuously review the situation and be proactive in communicating strategies of self-efficacy. Regarding the New Zealand potential foot and mouth disease case, a clear argument exists for why risk communicators must acknowledge and reinforce the unknown when framing messages for the public. Similarly, the collaboration and coordination among agencies with credible information sources helped the New Zealand crisis leaders build support among the various stakeholders affected by the potential contamination. As for Odwalla, the company was forced to acknowledge diverse levels of risk tolerance as the complexity of the situation unfolded. Similarly, a recognition of the need for a culture-centered approach would have enhanced the communication of ConAgra Foods with consumers and demonstrated a commitment to safety over profit. The following five chapters serve as examples of case studies involving risk communication.

• Patton M. Q. Qualitative research and evaluation methods. 3. Thousand Oaks, CA: Sage; 2002. [ Google Scholar ]
• Rogers E. M. Diffusion of innovations. 5. New York, NY: Free Press; 2003. [ Google Scholar ]
• Seeger M. W. Best practices in crisis communication: An expert panel process. Journal of Applied Communication Research. 2006; 34 (3):232–244. doi: 10.1080/00909880600769944. [ CrossRef ] [ Google Scholar ]
• Seeger M. W., Sellnow T. L., Ulmer R. R., editors. Crisis communication and the public health. Cresskill, NJ: Hampton Press, Inc.; 2008. [ Google Scholar ]
• Sellnow T. L., Littlefield R. S., editors. Lessons learned about protecting America's food supply: Case studies in crisis communication. Fargo, ND: Institute for Regional Studies; 2005. [ Google Scholar ]
• Stake R. E. Case studies. In: Denzin N. K., Lincoln Y. S., editors. Handbook of qualitative research. 2. Thousand Oaks, CA: Sage; 2000. pp. 435–454. [ Google Scholar ]
• Tuschman M. L., Anderson P. Managing strategic innovation and change: A collection of readings. New York: Oxford University Press; 1997. [ Google Scholar ]
• Ulmer R. R., Sellnow T. L., Seeger M. W. Effective crisis communication: Moving from crisis to opportunity. Thousand Oaks, CA: Sage; 2007. [ Google Scholar ]
• Yin R. K. Case study research: Design and methods. 3. Thousand Oaks, CA: Sage; 2003. [ Google Scholar ]

Research Writing and Analysis

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Writing a Case Study

What is a case study?

A Case study is: 

• An in-depth research design that primarily uses a qualitative methodology but sometimes​​ includes quantitative methodology.
• Used to examine an identifiable problem confirmed through research.
• Used to investigate an individual, group of people, organization, or event.
• Used to mostly answer "how" and "why" questions.

What are the different types of case studies?

Note: These are the primary case studies. As you continue to research and learn

about case studies you will begin to find a robust list of different types. 

Who are your case study participants?

What is triangulation ? 

Validity and credibility are an essential part of the case study. Therefore, the researcher should include triangulation to ensure trustworthiness while accurately reflecting what the researcher seeks to investigate.

How to write a Case Study?

When developing a case study, there are different ways you could present the information, but remember to include the five parts for your case study.

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• Published: 27 June 2011

The case study approach

• Sarah Crowe 1 ,
• Kathrin Cresswell 2 ,
• Ann Robertson 2 ,
• Guro Huby 3 ,
• Anthony Avery 1 &
• Aziz Sheikh 2  

BMC Medical Research Methodology volume  11 , Article number:  100 ( 2011 ) Cite this article

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The case study approach allows in-depth, multi-faceted explorations of complex issues in their real-life settings. The value of the case study approach is well recognised in the fields of business, law and policy, but somewhat less so in health services research. Based on our experiences of conducting several health-related case studies, we reflect on the different types of case study design, the specific research questions this approach can help answer, the data sources that tend to be used, and the particular advantages and disadvantages of employing this methodological approach. The paper concludes with key pointers to aid those designing and appraising proposals for conducting case study research, and a checklist to help readers assess the quality of case study reports.

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Introduction

The case study approach is particularly useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context. Our aim in writing this piece is to provide insights into when to consider employing this approach and an overview of key methodological considerations in relation to the design, planning, analysis, interpretation and reporting of case studies.

The illustrative 'grand round', 'case report' and 'case series' have a long tradition in clinical practice and research. Presenting detailed critiques, typically of one or more patients, aims to provide insights into aspects of the clinical case and, in doing so, illustrate broader lessons that may be learnt. In research, the conceptually-related case study approach can be used, for example, to describe in detail a patient's episode of care, explore professional attitudes to and experiences of a new policy initiative or service development or more generally to 'investigate contemporary phenomena within its real-life context' [ 1 ]. Based on our experiences of conducting a range of case studies, we reflect on when to consider using this approach, discuss the key steps involved and illustrate, with examples, some of the practical challenges of attaining an in-depth understanding of a 'case' as an integrated whole. In keeping with previously published work, we acknowledge the importance of theory to underpin the design, selection, conduct and interpretation of case studies[ 2 ]. In so doing, we make passing reference to the different epistemological approaches used in case study research by key theoreticians and methodologists in this field of enquiry.

This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables 1 , 2 , 3 and 4 ) and those of others to illustrate our discussion[ 3 – 7 ].

What is a case study?

A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.

Stake's work has been particularly influential in defining the case study approach to scientific enquiry. He has helpfully characterised three main types of case study: intrinsic , instrumental and collective [ 8 ]. An intrinsic case study is typically undertaken to learn about a unique phenomenon. The researcher should define the uniqueness of the phenomenon, which distinguishes it from all others. In contrast, the instrumental case study uses a particular case (some of which may be better than others) to gain a broader appreciation of an issue or phenomenon. The collective case study involves studying multiple cases simultaneously or sequentially in an attempt to generate a still broader appreciation of a particular issue.

These are however not necessarily mutually exclusive categories. In the first of our examples (Table 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables 2 , 3 and 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 – 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].

What are case studies used for?

According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables 2 and 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.

Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].

How are case studies conducted?

Here, we focus on the main stages of research activity when planning and undertaking a case study; the crucial stages are: defining the case; selecting the case(s); collecting and analysing the data; interpreting data; and reporting the findings.

Defining the case

Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.

Selecting the case(s)

The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.

For an instrumental case study, selecting a "typical" case can work well[ 8 ]. In contrast to the intrinsic case study, the particular case which is chosen is of less importance than selecting a case that allows the researcher to investigate an issue or phenomenon. For example, in order to gain an understanding of doctors' responses to health policy initiatives, Som undertook an instrumental case study interviewing clinicians who had a range of responsibilities for clinical governance in one NHS acute hospital trust[ 17 ]. Sampling a "deviant" or "atypical" case may however prove even more informative, potentially enabling the researcher to identify causal processes, generate hypotheses and develop theory.

In collective or multiple case studies, a number of cases are carefully selected. This offers the advantage of allowing comparisons to be made across several cases and/or replication. Choosing a "typical" case may enable the findings to be generalised to theory (i.e. analytical generalisation) or to test theory by replicating the findings in a second or even a third case (i.e. replication logic)[ 1 ]. Yin suggests two or three literal replications (i.e. predicting similar results) if the theory is straightforward and five or more if the theory is more subtle. However, critics might argue that selecting 'cases' in this way is insufficiently reflexive and ill-suited to the complexities of contemporary healthcare organisations.

The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.

It is also important to consider in advance the likely burden and risks associated with participation for those who (or the site(s) which) comprise the case study. Of particular importance is the obligation for the researcher to think through the ethical implications of the study (e.g. the risk of inadvertently breaching anonymity or confidentiality) and to ensure that potential participants/participating sites are provided with sufficient information to make an informed choice about joining the study. The outcome of providing this information might be that the emotive burden associated with participation, or the organisational disruption associated with supporting the fieldwork, is considered so high that the individuals or sites decide against participation.

In our example of evaluating implementations of electronic health record systems, given the restricted number of early adopter sites available to us, we sought purposively to select a diverse range of implementation cases among those that were available[ 5 ]. We chose a mixture of teaching, non-teaching and Foundation Trust hospitals, and examples of each of the three electronic health record systems procured centrally by the NPfIT. At one recruited site, it quickly became apparent that access was problematic because of competing demands on that organisation. Recognising the importance of full access and co-operative working for generating rich data, the research team decided not to pursue work at that site and instead to focus on other recruited sites.

Collecting the data

In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 – 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table 2 )[ 4 ].

Brazier and colleagues used a mixed-methods case study approach to investigate the impact of a cancer care programme[ 22 ]. Here, quantitative measures were collected with questionnaires before, and five months after, the start of the intervention which did not yield any statistically significant results. Qualitative interviews with patients however helped provide an insight into potentially beneficial process-related aspects of the programme, such as greater, perceived patient involvement in care. The authors reported how this case study approach provided a number of contextual factors likely to influence the effectiveness of the intervention and which were not likely to have been obtained from quantitative methods alone.

In collective or multiple case studies, data collection needs to be flexible enough to allow a detailed description of each individual case to be developed (e.g. the nature of different cancer care programmes), before considering the emerging similarities and differences in cross-case comparisons (e.g. to explore why one programme is more effective than another). It is important that data sources from different cases are, where possible, broadly comparable for this purpose even though they may vary in nature and depth.

Analysing, interpreting and reporting case studies

Making sense and offering a coherent interpretation of the typically disparate sources of data (whether qualitative alone or together with quantitative) is far from straightforward. Repeated reviewing and sorting of the voluminous and detail-rich data are integral to the process of analysis. In collective case studies, it is helpful to analyse data relating to the individual component cases first, before making comparisons across cases. Attention needs to be paid to variations within each case and, where relevant, the relationship between different causes, effects and outcomes[ 23 ]. Data will need to be organised and coded to allow the key issues, both derived from the literature and emerging from the dataset, to be easily retrieved at a later stage. An initial coding frame can help capture these issues and can be applied systematically to the whole dataset with the aid of a qualitative data analysis software package.

The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table 4 )[ 6 ].

Case study findings can have implications both for theory development and theory testing. They may establish, strengthen or weaken historical explanations of a case and, in certain circumstances, allow theoretical (as opposed to statistical) generalisation beyond the particular cases studied[ 12 ]. These theoretical lenses should not, however, constitute a strait-jacket and the cases should not be "forced to fit" the particular theoretical framework that is being employed.

When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].

What are the potential pitfalls and how can these be avoided?

The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.

Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table 8 )[ 8 , 18 – 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table 9 )[ 8 ].

Conclusions

The case study approach allows, amongst other things, critical events, interventions, policy developments and programme-based service reforms to be studied in detail in a real-life context. It should therefore be considered when an experimental design is either inappropriate to answer the research questions posed or impossible to undertake. Considering the frequency with which implementations of innovations are now taking place in healthcare settings and how well the case study approach lends itself to in-depth, complex health service research, we believe this approach should be more widely considered by researchers. Though inherently challenging, the research case study can, if carefully conceptualised and thoughtfully undertaken and reported, yield powerful insights into many important aspects of health and healthcare delivery.

Yin RK: Case study research, design and method. 2009, London: Sage Publications Ltd., 4

Google Scholar  

Keen J, Packwood T: Qualitative research; case study evaluation. BMJ. 1995, 311: 444-446.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Sheikh A, Halani L, Bhopal R, Netuveli G, Partridge M, Car J, et al: Facilitating the Recruitment of Minority Ethnic People into Research: Qualitative Case Study of South Asians and Asthma. PLoS Med. 2009, 6 (10): 1-11.

Article   Google Scholar  

Pinnock H, Huby G, Powell A, Kielmann T, Price D, Williams S, et al: The process of planning, development and implementation of a General Practitioner with a Special Interest service in Primary Care Organisations in England and Wales: a comparative prospective case study. Report for the National Co-ordinating Centre for NHS Service Delivery and Organisation R&D (NCCSDO). 2008, [ http://www.sdo.nihr.ac.uk/files/project/99-final-report.pdf ]

Robertson A, Cresswell K, Takian A, Petrakaki D, Crowe S, Cornford T, et al: Prospective evaluation of the implementation and adoption of NHS Connecting for Health's national electronic health record in secondary care in England: interim findings. BMJ. 2010, 41: c4564-

Pearson P, Steven A, Howe A, Sheikh A, Ashcroft D, Smith P, the Patient Safety Education Study Group: Learning about patient safety: organisational context and culture in the education of healthcare professionals. J Health Serv Res Policy. 2010, 15: 4-10. 10.1258/jhsrp.2009.009052.

Article   PubMed   Google Scholar  

van Harten WH, Casparie TF, Fisscher OA: The evaluation of the introduction of a quality management system: a process-oriented case study in a large rehabilitation hospital. Health Policy. 2002, 60 (1): 17-37. 10.1016/S0168-8510(01)00187-7.

Stake RE: The art of case study research. 1995, London: Sage Publications Ltd.

Sheikh A, Smeeth L, Ashcroft R: Randomised controlled trials in primary care: scope and application. Br J Gen Pract. 2002, 52 (482): 746-51.

PubMed   PubMed Central   Google Scholar  

King G, Keohane R, Verba S: Designing Social Inquiry. 1996, Princeton: Princeton University Press

Doolin B: Information technology as disciplinary technology: being critical in interpretative research on information systems. Journal of Information Technology. 1998, 13: 301-311. 10.1057/jit.1998.8.

George AL, Bennett A: Case studies and theory development in the social sciences. 2005, Cambridge, MA: MIT Press

Eccles M, the Improved Clinical Effectiveness through Behavioural Research Group (ICEBeRG): Designing theoretically-informed implementation interventions. Implementation Science. 2006, 1: 1-8. 10.1186/1748-5908-1-1.

Article   PubMed Central   Google Scholar  

Netuveli G, Hurwitz B, Levy M, Fletcher M, Barnes G, Durham SR, Sheikh A: Ethnic variations in UK asthma frequency, morbidity, and health-service use: a systematic review and meta-analysis. Lancet. 2005, 365 (9456): 312-7.

Sheikh A, Panesar SS, Lasserson T, Netuveli G: Recruitment of ethnic minorities to asthma studies. Thorax. 2004, 59 (7): 634-

CAS   PubMed   PubMed Central   Google Scholar  

Hellström I, Nolan M, Lundh U: 'We do things together': A case study of 'couplehood' in dementia. Dementia. 2005, 4: 7-22. 10.1177/1471301205049188.

Som CV: Nothing seems to have changed, nothing seems to be changing and perhaps nothing will change in the NHS: doctors' response to clinical governance. International Journal of Public Sector Management. 2005, 18: 463-477. 10.1108/09513550510608903.

Lincoln Y, Guba E: Naturalistic inquiry. 1985, Newbury Park: Sage Publications

Barbour RS: Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?. BMJ. 2001, 322: 1115-1117. 10.1136/bmj.322.7294.1115.

Mays N, Pope C: Qualitative research in health care: Assessing quality in qualitative research. BMJ. 2000, 320: 50-52. 10.1136/bmj.320.7226.50.

Mason J: Qualitative researching. 2002, London: Sage

Brazier A, Cooke K, Moravan V: Using Mixed Methods for Evaluating an Integrative Approach to Cancer Care: A Case Study. Integr Cancer Ther. 2008, 7: 5-17. 10.1177/1534735407313395.

Miles MB, Huberman M: Qualitative data analysis: an expanded sourcebook. 1994, CA: Sage Publications Inc., 2

Pope C, Ziebland S, Mays N: Analysing qualitative data. Qualitative research in health care. BMJ. 2000, 320: 114-116. 10.1136/bmj.320.7227.114.

Cresswell KM, Worth A, Sheikh A: Actor-Network Theory and its role in understanding the implementation of information technology developments in healthcare. BMC Med Inform Decis Mak. 2010, 10 (1): 67-10.1186/1472-6947-10-67.

Article   PubMed   PubMed Central   Google Scholar  

Malterud K: Qualitative research: standards, challenges, and guidelines. Lancet. 2001, 358: 483-488. 10.1016/S0140-6736(01)05627-6.

Article   CAS   PubMed   Google Scholar  

Yin R: Case study research: design and methods. 1994, Thousand Oaks, CA: Sage Publishing, 2

Yin R: Enhancing the quality of case studies in health services research. Health Serv Res. 1999, 34: 1209-1224.

Green J, Thorogood N: Qualitative methods for health research. 2009, Los Angeles: Sage, 2

Howcroft D, Trauth E: Handbook of Critical Information Systems Research, Theory and Application. 2005, Cheltenham, UK: Northampton, MA, USA: Edward Elgar

Book   Google Scholar  

Blakie N: Approaches to Social Enquiry. 1993, Cambridge: Polity Press

Doolin B: Power and resistance in the implementation of a medical management information system. Info Systems J. 2004, 14: 343-362. 10.1111/j.1365-2575.2004.00176.x.

Bloomfield BP, Best A: Management consultants: systems development, power and the translation of problems. Sociological Review. 1992, 40: 533-560.

Shanks G, Parr A: Positivist, single case study research in information systems: A critical analysis. Proceedings of the European Conference on Information Systems. 2003, Naples

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Acknowledgements

We are grateful to the participants and colleagues who contributed to the individual case studies that we have drawn on. This work received no direct funding, but it has been informed by projects funded by Asthma UK, the NHS Service Delivery Organisation, NHS Connecting for Health Evaluation Programme, and Patient Safety Research Portfolio. We would also like to thank the expert reviewers for their insightful and constructive feedback. Our thanks are also due to Dr. Allison Worth who commented on an earlier draft of this manuscript.

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AS conceived this article. SC, KC and AR wrote this paper with GH, AA and AS all commenting on various drafts. SC and AS are guarantors.

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Crowe, S., Cresswell, K., Robertson, A. et al. The case study approach. BMC Med Res Methodol 11 , 100 (2011). https://doi.org/10.1186/1471-2288-11-100

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Using unfolding case studies to develop critical thinking for Graduate Entry Nursing students: an educational design research study

• Rachel Macdiarmid   ORCID: orcid.org/0000-0003-4791-7417 1 ,
• Eamon Merrick   ORCID: orcid.org/0000-0003-4269-6360 2 , 3 &
• Rhona Winnington   ORCID: orcid.org/0000-0002-6504-2856 1  

BMC Nursing volume  23 , Article number:  399 ( 2024 ) Cite this article

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Graduate Entry Nursing (GEN) programmes have been introduced as another entry point to nurse registration. In the development of a new GEN programme, a problem-based approach to learning was used to develop critical thinking and clinical reasoning skills of motivated and academically capable students.

To explore and evaluate the design and delivery of course material delivered to GEN students embedded in authentic learning pedagogy from the perspectives of both GEN students and academic staff using an unfolding case study approach.

An educational design research approach was used to explore the learning experiences of GEN students using an unfolding case study approach situated in experiential pedagogy and the teaching experiences of the academics who designed it. Data were collected through semi-structured interviews with students once they had finished the course and weekly reflective diary recordings by academic staff throughout implementation. Thematic analysis was used to analyse the data.

Student reflections highlighted that this cohort had insight into how they learned and were comfortable voicing their needs to academic staff. While the unfolding case studies were not liked by all participants, for some it offered a unique learning opportunity; particularly when scaffolded with podcasts, simulation labs, tutorials and clinical placements. Staff reflections primarily aligned with student experiences.

The gaps highlighted in the delivery of the course suggest that a blended pedagogical approach to graduate entry nurse education is required. Specifically, GEN students are aware of the learning needs and are happy to express these to academic staff, thus suggesting that engaging with a co-design curriculum approach will benefit future cohorts.

Peer Review reports

Graduate entry nursing students begin their degrees as experienced learners and must develop critical thinking skills within the shortened degree time frame.

What is already known

Graduate entry students are experienced and academically capable learners who begin with a diverse range of life and career experiences.

What this paper adds

Graduate entry students would benefit by being involved in curriculum design to acknowledge the unique skill set that they bring.

Introduction

Graduate Entry Nursing (GEN) degrees, or second degrees leading to eligibility for nursing registration, have recently been introduced to New Zealand. GEN students are known to be academically capable, motivated, and driven, bringing with them a range of life experiences, and have often had significant careers before enrolment [ 1 , 2 ]. Previous research has identified that teaching and learning methods must be carefully planned and innovative [ 1 ].

Pre-registration nursing education programmes prepare nursing students to provide safe nursing care with crucial skills expected of nursing graduates, including critical thinking and clinical reasoning. Clinical reasoning enables students to approach clinical issues with a problem-solving lens that relies on gathering assessment data and intervening and evaluating the patient’s response to the intervention [ 3 ].

Problem-Based Learning (PBL) aligns with the fundamental elements of authentic learning approaches [ 4 ], where learning is situated in real-world contexts [ 5 ]. Problem-based learning is considered to be an experiential teaching and learning approach that helps students develop a critical lens and clinical reasoning skills [ 6 , 7 ]. The use of PBL in nursing education is well established with previous research focused on students’ experiences and satisfaction [ 8 ]; factors that facilitate or hinder students' learning [ 9 ]; and the development of critical thinking skills [ 10 ].

Graduate entry nursing students report enjoyment of the active learning sets that enabled discussion surrounding case studies, scenarios, and practice issues [ 11 ]. Cangelosi’s [ 12 ] phenomenological study found that although time-poor, GEN students welcomed learning opportunities that were not traditional and facilitated their development and growth.

However, there is conflicting evidence regarding the effectiveness of PBL in nursing. For example, McCormick et al. [ 13 ] compared undergraduate student performance using differing teaching approaches, such as unfolding simulation scenarios versus recorded lectures and found these to be of benefit to students. Carter and Welch [ 14 ] compared the results of associate degree nursing students who attended lectures to those whose learning was informed by an unfolding case study. In contrast to McCormick’s et al.’s [ 13 ] earlier positive results, these authors found both groups of students performed worse in the post-test.

As previous research has identified that new graduate nurses do not always have critical thinking skills, using an unfolding case study approach can reflect the reality of clinical practice where not all the relevant information is known at the first encounter with the patient [ 14 , 15 , 16 ].

Nonetheless, while several studies have investigated the use of unfolding case studies in undergraduate preregistration programmes there is little evidence that supports the use of these with more academically capable GEN students. This article reports on a qualitative interpretivist study that used an educational design methodology to explore the experiences of GEN students who participated in the programme of learning and the experiences of the academics who designed it.

Educational Design Research (EDR) is an iterative, pragmatic, and reflective methodology well suited to small projects [ 17 ]. It has arisen from design-based research and can include both quantitative and qualitative data collection methods. EDR was selected as it fitted with our desire to develop new ways of teaching alongside gaining feedback from both academic staff and students. In the first phase of this research, we redesigned the teaching and learning strategies for a component of the GEN programme [ 18 ].

EDR has four phases (Table  1 ) [ 17 ]:

Aims and objectives

The study aimed to explore and evaluate the design and delivery of course material delivered to GEN students embedded in authentic learning pedagogy from the perspectives of both GEN students and academic staff using an unfolding case study approach.

Theoretical framework

To enable the development of clinical reasoning skills a scaffolded learning approach was implemented that involved unfolding case studies designed to represent the health needs of the New Zealand population, thus, encouraging critical thinking. Unfolding case studies reflective of situations that students might face in the future were used to encourage students to consider and analyse information, provoke further questioning and identify the information required to narrow their inquiries [ 14 , 15 ]. Supported by this evidence the academic staff built a learning environment where a regular teaching schedule (two days of lectures and one day of clinical labs per week), was complemented with online resources. Initial questions about the case study were provided on the learning management system. Students attended simulations where they responded to the case and answered questions critical to unpacking the ‘patients’ reality. Alongside the unfolding case studies were podcasts where experts were interviewed on topics related to the case. Tutorials enabled students to collaboratively construct answers and share their perspectives; at the end of each week students shared their answers in an online discussion forum.

Methods and setting

This study was conducted at an education facility in New Zealand offering undergraduate and GEN programmes. The participants are academics involved in the design and delivery of the course and one cohort of students of the GEN programme. This article reports on Phase 2 and 3 of the EDR approach, the academic staff’s reflective diary during course delivery, and students' feedback after the course was completed the first time. The methods were reported using the Consolidated Criteria for Reporting Qualitative Studies (COREQ) [ 19 ].

Participants

Purposeful sampling was used as the researchers were keen to explore the experiences of a specific GEN cohort [ 20 ]. Academic staff involved in the weekly reflective diaries are also the research team ( n  = 3). All students in the identified cohort ( n  = 7) were invited to participate, totalling ten possible participants. Student participants were approached via an advertisement on the university’s learning management system. Students were asked to contact the research assistant, who was separate from the academic staff and was not involved in the delivery of the GEN programme; five students agreed to participate. A $20 petrol voucher was offered to those who participated.

Data collection and analysis

In keeping with education design methodology, the authors met weekly to reflect on their experiences of delivering the content and guiding students. The weekly reflective conversations, between 60–90 min in length, followed a simple format of ‘what worked, what didn’t work, and what would we (as academic staff) change?’ Face to face student interviews were conducted by the research assistant at a time and place convenient to the students using semi-structured questions that were developed by the research team (see Additional file 1 ).

The semi-structured interviews ( n  = 5) and reflective meetings ( n  = 9) were recorded and transcribed verbatim by a research assistant who had signed a confidentiality agreement. All identifying information was deleted from the transcripts by the research assistant before the research team reviewed the data; each recording and transcript was allocated a unique identifier, for example ‘participant one’.

Thematic analysis [ 21 , 22 ] was used to analyse the data. First, the research team independently read the transcribed interviews to familiarise themselves with the data and identified initial codes. Second, the researchers met and reviewed all transcripts to identify themes and reached consensus on the themes emerging from the data. Themes were established once more than 50% of the participants stated the same issue/thought/perception. A matrix was developed whereby common themes were identified, with quotes demonstrating the themes collated to establish an audit trail.

Reflexivity

Central to this study given the proximity of staff to this student cohort, a reflexive stance was essential. Reflexivity is an engendered practice and was used in this instance not to influence the direction and outcome of the research but to allow the researchers to engage in the data to produce viable and valuable outcomes for future staff and students. Specifically, this reflexive practice provided a means for the research to be rigorous through the consideration of the vulnerability of the participating student cohort, thus inciting reflection-before-action [ 23 ].

Ethical considerations

Ethical approval for this study was obtained from the Auckland University of Technology Ethics Committee (AUTEC) (19/233). Given the potential power differential in the student/staff relationship present, participants were approached via an online advertisement and followed up by an independent research assistant. This is key to the success of the project, as such research undertakings have the potential for conflict of interest to exist [ 24 ]. The academic staff recordings were also undertaken with the knowledge that these would remain confidential to the participants and transcriber only, with a memorandum of understanding completed to this effect. Participant information sheets were given to students interested in joining the study to ensure they knew what it entailed and how their safety and identity would be managed. Written consent was obtained before the interviews were undertaken, with oral consent obtained at the beginning of each interview.

Three dominant themes emerged, which focused on the experiences of both GEN students and teaching staff. These were:

Reflective learning: Students and staff ability to clarify what worked and what did not work

Evaluation of learning: Students and staff being insightful about their ways of learning and needs

Challenges: Planning and delivering appropriate content for GEN students is challenging for teaching staff.

Within these overarching themes, subthemes were developed and will be presented in the following data results (Table 2 ).

Reflective learning

The exploration of student and staff experiences and responses to the unfolding case studies unearths what worked and what was problematic for both parties.

Unfolding case study as problem-based approach

The student experiences of using an unfolding case study approach were divided. Some students enjoyed the case scenarios but did not necessarily find them beneficial in terms of knowledge advancement as.

“ I personally, like the case studies but personally I didn’t really find that they enhanced my learning in like the clinical setting ” (P1)

or that they were relevant to clinical practice in that.

“… some of it was definitely relatable but I just found it was very different in the clinical setting compared with doing this theoretical case setting ” (P1).

A second student supported this idea that the case studies did not add practical clinical knowledge value as.

“ I mean for me the case studies weren’t challenging…I didn’t think the case studies added anything extra into my practice, they didn’t challenge my clinical reasoning or anything like that ” (P2).

Of note was that those students with previous professional healthcare backgrounds found the use of an unfolding case study approach problematic in that.

“ I found that quite a challenge. I think because with my clinical background I was sort of going straight into, yeah like I wanted more information so you know I probably would have preferred…to have a different case study every week or have all the information…and I’d be like well what about this, what about that? ” (P5).

Participant One, however, noted that while the case studies may not have added knowledge value, they were helpful at times as.

“ …one example is we learnt about arterial blood gases and then I was on placement I came across that literally [on] day one, so was really nice to be able to put something that I’d learnt in class into practice ” (P1).

While some students were less keen on the case study approach and found them hard work, others thought they provided opportunities to encourage discussion, clinical reasoning, and autonomous thinking as.

“ there was no right or wrong answer, you just had to prove your point to say I think it is this because of this, and someone else can say something else and just kind of still prove it because it was a quite grey [area] but I actually found that it really got us thinking ” (P3).

Moreover, the same participant acknowledged that.

“…I think that’s the whole idea of the course [GEN Programme] because at this level they shouldn’t be spoon-feeding you…you should be able to think for yourself and reason things out ” (P3).

Although some discord was present with regard to the case study approach, one participant did acknowledge the value of being able to break down a huge scenario into manageable sections to enhance understanding and clinical decision-making, as.

“ when you break it down it makes it easier to kind of work out what you’re going to do and what steps you’re going to do ” (P4), and that “ because you start looking at the smaller things that you need to do rather than just the big bits ” (P4).

It appears, however, that staff involved in the programme of learning were pleased with the overall notion that problem-based learning approach offered a ‘practical’ means through which to discuss what is the hands-on job of nursing. Specifically,

“ the second session around child abuse and recognising child abuse…took me a bit by surprise as I wasn’t expecting that to go very well and it went extraordinarily well, mostly because it was case based again and story based ” (L1).

Moreover, with regard to encouraging discussion and clinical reasoning at a postgraduate level,

“ I think we’ve really pulled out the difference [of] what we’re expecting of them [GEN students] as opposed to what they may have been used to” (L1).

Use of podcasts

While the use of technology is not necessarily a completely new strategy in tertiary education, here we have linked podcasts recorded with experts in their fields which related to the unfolding case studies, Again, however, there was division in the value of podcast recordings, with some students really enjoying them, saying.

“ I liked the podcasts yeah, I found the podcasts really good especially when there was [sic] different people talking about it, yeah...podcasts are good, like to just chuck on in the car or at the gym ” (P2).

Moreover, some found them easy to listen to because.

“… it’s a different way to learn because like you’ve got YouTube videos and you’ve got books and stuff but podcasts are kind of like easy ” (P2).

Some students found the podcasts particularly engaging saying.

…I just remember listening to it and I think I was in the car and I had stopped because I was on my way home…and I was still listening to it in the garage like when I was home and I was like oh this is a really interesting podcast ” (P2).

Participant three also thought podcasts a positive addition to the resources saying.

“ yeah they were helpful…there was one I listened to…they were talking about dying…I know that [one of the lecturers’] kind of research is kind of talking about death, euthanasia and all this kind of thing, and for some reasons, I don’t know why, maybe that’s why I still remember, I can say it’s the only podcast I really listened to and it was really good because it gave me a good insight as to what is happening… ” (P3)

This positive response was also noted in face-to-face class time as one staff member reported that.

“ they [the students] loved the person who was interviewed, and the feedback was it was really nice to hear a conversation about different perspectives ” (L1).

Yet, not all students were of this opinion, with some advising the podcasts were too long (approximately 60 min each), that they can be distracting, that they preferred videos and images or an in-person discussion, saying.

“ I find podcasts…I tend to switch off a bit, a bit quicker than if I was watching something, I would probably prefer, rather than watching a podcast [sic] I’d rather have an in-class discussion with the person” (P4).

Participant one said that they too struggled with podcasts because.

“ I’m more visual so I like to look at things and see like a slide I guess or what they’re talking about or, so I sort of zone out when it’s just talking and nothing to look at, so that’s what I personally struggle with, they [podcasts] are helpful it’s just I’m more a visual learner ” (P1).

While there were some negative responses to the podcasts, another participant acknowledged their value but offered their own solutions to learning, saying that.

“ I listened to a few podcasts that were put up, because they’re just easy to listen to ” (P2).

but felt that overall there were insufficient resources made available to students and therefore.

“ just went to YouTube and just, any concepts that I was unfamiliar with or stuff in class that we went over and when I went home I was like [I have] no idea what they talked about, I just found my own videos on YouTube… ” (P2).

Evaluation of learning

Learning experiences are unique to each GEN student, as are those experienced by the teaching staff. The data collected highlighted this clearly from both perspectives, offering a particularly strong insight into how this cohort of students’ function.

Approaches to learning

It was evident that these GEN students were aware of their approach to learning and that perhaps the structure of the teaching module did not align with their needs as.

“ I’m not really the best at utilising online things I’m a really hands on learner and things like a lecture…but you know if it’s yeah, more like class time, it’s sort of more my, my learning style [I] guess ” (P5).

A number of students were able to identify that they were visual learners as.

“ I use videos more because I guess I’m more of a visual learner as well and I learn better by seeing things instead of reading a huge article, I think that [videos] it helps me a bit more” (P4).

Another student, however, preferred a discussion based approach as opposed to either videos or podcasts saying that.

“ if it’s interesting, if it’s a topic that you can like relate to [through a podcast] or something it’s fine, but for me I just switch off not really taking a lot of the information [in] whereas in a discussion setting you can ask questions and you can interact with the person, yeah I find that would be a bit more helpful ” (P4).

This approach to learning through discussion was also noted when the teaching staff reflected on their experiences in that in one teaching session the GEN students.

“ were engaged, they were round a table with the second speaker talking and what I think enabled the discussion was that she [the speaker] was using her data as stories and so she was reading them, actually she got them [the students] to read them out” (L3).

The notion of learning styles, however, was not as linear as being visual or auditory or practical, as one student noted that a combination of styles was preferable to enhance learning, saying that.

“ if we weren’t able to have lectures like a recorded lecture so that there was a PowerPoint and just someone actually talking you through it, like I know there’s the YouTube videos…some of them were a little bit helpful, but like I just felt that sometimes we missed the teaching aspect of it. There’s a lot of self-directed stuff but definitely like a recorded lecture every week to go along with the readings and extra videos to watch ” (P5).

Students as insightful and engaged

While GEN students are known for their tenacity and ability to cope with the pressure and fast paced delivery, some students discovered that this did not necessarily equate with their preferred approach to learning. This cohort of GEN students were insightful in terms of their strengths and weaknesses in relation to knowledge acquisition. The use of the unfolding case studies, however, caused some frustrations as.

“ for me it was challenging in the fact that I felt I actually got frustrated because I’m thinking well I want to know this, I want to know that and yeah not getting all the information that I wanted at the time ” (P5).

This participant went further, saying that.

“ I definitely found that difficult [lack of information] I felt like [I] wasn’t getting as much information as I wanted to be able to make my clinical decisions ” (P5),

however this may have been due to the student’s background as their.

“my background is in paramedicine ” where “ we get a lot of information in a very short amount of time ” (P5).

Some fundamental issues were raised by the participants in terms of how much study is required for them to acquire the new knowledge. As one student highlighted,

“ I have a really terrible memory, so I kind of need to listen to things a few times or write it down and then watch a video and do some more reading and then like it’s good having another element to get into your brain you know ” (P2).

For one student, a solution to this was to ensure they did their preparation before attending class as.

“ you’re supposed to have read these things before coming to class, some people don’t but my kind of person, I’d read before coming to class and I tended to answer those questions so the critical, analytical part of me would be trying to find out and come up with a reasonable answer…” (P3).

For another participant, they took an alternative pathway to learning as they.

“ I just watch it and I don’t take [it in], it just sits in the back of my head because sometimes it’s building on top of previous knowledge so just, I just watch it to see if I can gain anything from that, I don’t necessarily take down notes or anything, but I just watch it so that it’s there you know ” (P4).

The pace of content delivery appeared problematic for some students, especially in relation to the practical sessions, with one student highlighting that.

“ personally I didn’t’ really like it and most of the time they were rushing, I was always like can I write this down to go back home to like really make sense of it and then sometimes obviously, sometimes I would have to say can I stay back and practice this thing again [as] I didn’t grab it as quickly as others did and the essence of the labs is that it’s grab all of these things ” (P3).

Challenges: Teaching staff experiences of GEN student learning

While on the whole the teaching staff were able to gauge the learning needs of this GEN cohort, the expectations of both parties did not always align, with one staff member reporting that.

“ the two biggest challenges was [sic] getting them [the students] to unpack already learned behaviour and [to] acknowledge their own limitations or bias ” (L1),

however by the end of the semester the same staff member reported that.

“ I think we made a lot of progress in getting them to acknowledge how they learn ” (L1).

Moreover, the challenges anticipated in teaching GEN students were not those that transpired in that.

“ I actually thought going into the first paper I was pretty excited as to how it was going to roll out, the problems I encountered were not the problems I anticipated ” (L3).

The vocality of this cohort was tangible, however, when content did not meet their needs, interest or expectations with the students saying,

“ that they didn’t do the materials because it wasn’t of interest to them and requested other teaching very much related to the assignment as opposed to anything else …” (L1).

It was expected that the GEN students would be participatory both in class and online irrespective of their ways of learning, but there was a difference in both responses and comfort with this form of engagement. One student that talked about the unfolding case study and the online component of assessment as being problematic said that.

“.. we had to put up about 250 words of something related to the case study every week and then we spoke to someone else, [I] didn’t really like the responses…I didn’t really like having to respond to someone else ” (P3).

Yet in contrast to this statement, the teaching staff were delighted that.

“…actually I got some fantastic questions from one of the students…emailed to me on Monday night about the case that was online for them, questions that I didn’t talk about in [the] lecture, I didn’t introduce the concept…they’re talking about concepts that are currently undergoing international clinical trials” (L1).

This study explored the experiences of both GEN students and academics using unfolding case studies situated in experiential learning pedagogy. The use of unfolding case studies supported with podcasts embraced our idea of developing content situated in real-life contexts. Learning was scaffolded using different teaching approaches such as podcasts, and experiential simulated learning, to offer learners multiple ways of engaging with content. Scaffolding is recognised as learning material being broken into smaller chunks of learning and in this way aligns with case-based learning [ 25 ]. In this way, we hoped that not only would students engage in problem-solving, and develop clinical decision-making skills [ 26 , 27 ], but that they would also achieve deep and lifelong learning and ultimately have an ‘aha’ moment when it all made sense.

Reflections on using an unfolding case study approach

Findings were divided, with some students enjoying the unfolding case studies and others describing them as not sufficiently challenging. The scaffolded learning approach that we developed incorporated a range of teaching approaches that enabled them to engage with the content in a way that fitted in with their lifestyle, even if the teaching method did not align with their individual learning preferences. Students reported differing views about the case studies; some enjoyed the unfolding nature while others wanted more context and direction to feel that they could make an informed clinical decision. Nonetheless, even though they did not like information being presented in smaller chunks one student recognised it meant they analysed the information they received more deeply.

Other learning tools such as podcasts were not always valued by participants and yet, the fact that students were able to provide feedback on their use does indicate that they at least attempted to engage with them.

Student reflections indicate that perhaps the use of unfolding case studies as a learning approach is not the solution to engagement, and that often more traditional teaching methods were preferred Indeed, Hobbs and Robinson’s [ 28 ] study of undergraduate nursing students in the US supported Carter and Welch’s [ 14 ] findings that the use of unfolding case studies were of no direct benefit, whilst Ellis et al.’s., [ 29 ] study confirmed that for final year nurse practitioner students unfolding case studies were beneficial in developing critical thinking and stimulating clinical reasoning. Considering these two conflicting findings, further consideration is needed of how to engage highly motivated GEN students.

As such, our results suggest it can be difficult to predict the needs of the GEN students given the diversity of their previous academic qualifications, career, and often significant life experience they bring to the programme [ 30 , 31 ]. Interestingly students in this study simultaneously demonstrated insight into their needs supporting their previous academic study experience and felt sufficiently secure to voice them, which supports evidence found in D’Antonio et al.’s [ 32 ] study. This suggests that GEN students’ capabilities need to be embraced and incorporated when planning curriculum and scaffolding learning. Anecdotally, we have found that students embrace experiential learning such as that offered in simulation labs whether this involves the use of simulated manikins or not, it seems the hands-on learning offers not only the opportunity to experience simulated reality but also fosters collaboration and problem solving with peers that enables them to dwell in learning of what it is to be a nurse.

Graduate entry students recognised as experienced learners

Our students were not overwhelmingly supportive of the pedagogical approach of unfolding case studies we adopted. As previously recognised GEN students are experienced learners and whilst having differing educational backgrounds bring individual experience and knowledge of their own approach to their learning. Nonetheless, the value of their previous learning experience appears problematic in that those learned behaviours and attitudes need to be refocused to engage with learning how to become a nurse, as demonstrated in the academic staff reflections. Despite this background experience and perceived confidence, some students reflected that online engagement that involved exploring the case studies in discussion forums with colleagues was uncomfortable. This was surprising to the academic staff and contrasted sharply with their reflections on the activity but has been previously noted by Boling et al., [ 33 ].

Implications

Given the disparity that exists between student and academic staff experiences, as demonstrated in our study, co-designing content delivery may offer a progressive solution. By engaging ‘students as partners’ it offers them a much deeper level of involvement in future teaching delivery through collaboration and reciprocation of ideas, thus culminating in appropriate curriculum design [ 34 ]. Collaborating with students in course design might facilitate students learning as they become cognisant of the active engagement of academic staff [ 9 , 10 , 35 ]. In the future, we aim to involve students in any curriculum review and course development to ensure their perspectives influence curriculum design and content delivery.

Even so, our initial intention of scaffolding learning by offering different ways for students to engage with content is supported by recent research by Dong et al. [ 36 ] who found that students performed better academically in a flipped classroom. This point, in association with our findings, suggests that the best approach to content delivery for graduate entry nursing students is to ensure students are involved in curriculum and course design alongside the delivery of learning experiences that are well facilitated and supported by faculty so that students are aware of the expectations, required of them, and importantly how they will be assessed.

Limitations

We acknowledge that the sample size in this study is small in terms of generalisability. However, our findings offer interesting, detailed and in-depth insights into the experiences and needs of both GEN students and the academic staff involved in the development and delivery of educational material. Further work needs to be undertaken to evaluate the experiences of GEN students from a range of educational providers. A longitudinal study has been undertaken to explore the motivations and experiences of GEN students in Australasia [ 37 ], which will also support these findings regarding the learning needs of GEN students.

This study has provided a platform through which academics and GEN students can share their insights of teaching and learning experiences. The results offer a clear insight into what these students expect and need to expedite their learning and how teaching staff must respond. While participants' views were somewhat mixed in relation to the use of unfolding case studies and scaffolded learning these results demonstrate how GEN students are aware of their personal ways of learning and how this translates in terms of education needs. The sharing of these experiences provides an insightful lens through which to re-evaluate pedagogical approaches for GEN students. As such, we suggest that to meet the needs of GEN student’s not only is a blended pedagogical approach appropriate but expanding education design boundaries further through a co-design focused approach to GEN programme design.

Availability for data and materials

The datasets generated and analysed during the current study are not publicly available due privacy and ethical restrictions of the participants, but are available from the corresponding author on reasonable request.

Downey KM, Asselin ME. Accelerated master’s programs in nursing for non-nurses: An integrative review of students’ and faculty’s perceptions. J Prof Nurs. 2015;31(3):215–25. Available from: https://doi.org/10.1016/j.profnurs.2014.10.002 .

Pellico LH, Terrill E, White P, Rico J. Integrative review of graduate entry programs. J Nursg Educ. 2012;51(1):29–37. Available from:  https://doi.org/10.3928/01484834-20111130-01 .

Article   Google Scholar  

Levett-Jones T, Hoffman K, Dempsey J, Jeong SYS, Noble D, Norton CA, et al. The ‘five rights’ of clinical reasoning: An educational model to enhance nursing students’ ability to identify and manage clinically ‘at risk’ patients. Nurse Educ Today. 2010;30(6):515–20.

Article   PubMed   Google Scholar  

Smith SB, Kennedy S. Authentic teaching to promote active learning: Redesign of an online RN to BSN evidence-based practice nursing course. J Prof Nurs. 2020;36(2):56–61. Available from:  https://doi.org/10.1016/j.profnurs.2019.07.005 .

Herrington J, Reeves TC, Oliver R. Authentic Learning Environments. In: Spector J, Merrill M, Elen J, Bishop M, editors. Handbook of Research on Educational Communications and Technology. New York: Springer; 2014. p. 401–12.

Chapter   Google Scholar  

Herrington J, Reeves TC, Oliver R. A guide to authentic e-learning. London and New York: Routledge; 2009.

Book   Google Scholar  

Merisier S, Larue C, Boyer L. How does questioning influence nursing students’ clinical reasoning in problem-based learning? A scoping review.  Nurse Educ Today. 2018;65:108–15.

Spiers JA, Williams B, Gibson B, Kabotoff W, McIlwraith D, Sculley A, et al. Graduate nurses’ learning trajectories and experiences of problem-based learning: A focused ethnography study. Int J Nurs Stud. 2014;51(11):1462–71. Available from:  https://doi.org/10.1016/j.ijnurstu.2014.03.002 .

Article   CAS   PubMed   Google Scholar  

Svensson J, Axén A, Andersson EK, Hjelm M. Nursing students’ experiences of what influences achievement of learning outcomes in a problem-based learning context: A qualitative descriptive study. Nurs Open. 2021;8(4):1863–9.  Available from:  https://doi.org/10.1002/nop2.842 .

Article   PubMed   PubMed Central   Google Scholar  

Martyn J, Terwijn R, Kek MYCA, Huijser H. Exploring the relationships between teaching, approaches to learning and critical thinking in a problem-based learning foundation nursing course. Nurse Educ Today. 2014;34(5):829–35. Available from:  https://doi.org/10.1016/j.nedt.2013.04.023 .

Maddison C, Strang G. Do action learning sets facilitate collaborative, deliberative learning?: A focus group evaluation of Graduate Entry Pre-registration Nursing (GEN) students’ experience. Nurse Educ  Pract. 2018;28:285–91. Available from: https://doi.org/10.1016/j.nepr.2017.10.023 .

Cangelosi PR. Voices of graduates from second-degree baccalaureate nursing programmes. J Prof Nurs. 2007;23(2):91–7.

McCormick MJ, de Slavy JR, Fuller B. Embracing technology: Using an unfolding case simulation to enhance nursing students’ learning about Parkinson Disease. J Neurosci Nurs. 2013;45(1):14–20. Available from:  https://doi.org/10.1097/JNN.0b013e318275b220 .

Carter JT, Welch S. The effectiveness of unfolding case studies on ADN nursing students’ level of knowledge and critical thinking skills. Teach Learn Nurs. 2016;11:143–6. Available from: https://doi.org/10.1016/j.teln.2016.05.004 .

Carr KC. Using the unfolding case study in midwifery education. J Midwifery Womens Health. 2015;60:283–90. Available from: https://doi.org/10.1111/jmwh.12293 .

Yousey YK. The use of unfolding case studies: Innovation in online undergraduate nursing education. J Nurs EducPract. 2013;3(4):21–9. Available from:  https://doi.org/10.5430/jnep.v3n4p21 .

McKenney S, Reeves T. Conducting Educational Design Research. 2nd ed. New York: Routledge; 2018. Available from: http://ebookcentral.proquest.com .

Macdiarmid R, Winnington R, Cochrane T, Merrick E. Using educational design research to develop authentic learning for graduate entry students in New Zealand. Nurse Educ Prac. 2021; 51(01) Available from: https://doi.org/10.1016/j.nepr.2021.102965

Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care. 2007;19(6):349–57.

Parahoo K. Nursing Research: principles, process and issues. 3rd ed. London: Palgrave MacMillan; 2014.

Braun V, Clarke V, Hayfield N, Terry G. Thematic analysis. In: Liamputtong P, editor. Handbook of Research Methods in Health Social Sciences. Singapore: Springer; 2019. p. 843–60.

Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77–101. Available from: https://doi.org/10.1191/1478088706qp063oa .

Edwards S. Reflecting differently. New dimensions: reflection-before-action and reflection-beyond-action. Int Prac Dev J. 2017;7(1):1–14. Available from:  https://doi.org/10.19043/ipdj.71.002 .

Ferguson LM, Yonge O, Myrick F. Students’ involvement in faculty research: Ethical and methodological issues. Int J Qual Methods. 2004;3(4):56–8.

Coffman S, Iommi M, Morrow K. Scaffolding as active learning in nursing education. Teach Learn Nurs. 2023;18(1):232–7.

Bowman K. Use of online unfolding case studies to foster critical thinking. J Nurs Educ. 2017;56(11):701–2.

Gonslaz L, Nielsen A, Lasatar K. Developing students clinical reasoning skills: A faculty guide. J Nurse Educ. 2021;60(9):485–93. Available from: https://doi.org/10.3928/01484834-20210708-01 .

Hobbs J, Robinson C. Learning and Transfer Effects of an Unfolding Case Study in an Adult Health Nursing Course. Nurs Educ Perspect. 2022;43(1):47–8. Available from: https://doi.org/10.1097/01.NEP.0000000000000801 .

Ellis M, Hampton D, Makowski A, Falls C, Tovar E, Scott L, et al. Using unfolding case scenarios to promote clinical reasoning for nurse practitioner students. J Am Assoc Nurse Pract. 2023;35(1):55–62. Available from:  https://doi.org/10.1097/JXX.0000000000000806 .

Macdiarmid R, Turner R, Winnington R, McClunie-Trust P, Donaldson A, et al. What motivates people to commence a graduate entry nursing programme: a mixed method scoping review. BMC Nurs. 2021;20:47. Available from: https://doi.org/10.1186/s12912-021-00564-9 .

Winnington R, Shannon K, Turner R, Jarden R, McClunie-Trust P, Merrick E, Donaldson A, Macdiarmid R. Learning experiences of first year graduate entry nursing students in New Zealand and Australia: a qualitative case study. BMC Nurs. 2023;22:74. Available from: https://doi.org/10.1186/s12912-023-01233-9 .

D’Antonio P, Beal MW, Underwood PW, Ward FR, McKelvey M, Guthrie B, et al. Great expectations: points of congruencies and discrepancies between incoming accelerated second-degree nursing students and faculty. J Nurs Educ. 2010;49(12):713–7. Available from https://doi.org/10.3928/01484834-20100831-08 .

Boling EC, Hough M, Krinsky H, Saleem H, Stevens M. Cutting the distance in distance education: Perspective on what promotes positive online learning experiences. Int Higher Educ. 2012;15(2):118–26. Available from:  https://doi.org/10.1016/j.iheduc.2011.11.006 .

Bovill C. Co-creation in learning and teaching: the case for a whole-class approach in higher education. High Educ. 2020;79:1023–37. https://doi.org/10.1007/s10734-019-00453-w .

Paige JB, Smith RO. Nurse faculty experiences in problem-based learning: An interpretive phenomenologic analysis. Nurs Educ Perspect. 2013;34(4):233–9.

PubMed   Google Scholar  

Dong Y, Yin H, Du S, Wang A. The effects of flipped classroom characterized by situational and collaborative learning in a community nursing course: A quasi-experimental design. Nurse Educ Today. 2021;105. Available from:  https://doi.org/10.1016/j.nedt.2021.105037

Macdiarmid R, McClunie-Trust P, Shannon K, Winnington R, Donaldson A, Jarden R, et al. What Motivates People to Start a Graduate Entry Nursing Programme: An Interpretive Multi-Centred Case Study. SAGE Open Nursing. 2021;7. Available from:  https://doi.org/10.1177/23779608211011310 .

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Macdiarmid, R., Merrick, E. & Winnington, R. Using unfolding case studies to develop critical thinking for Graduate Entry Nursing students: an educational design research study. BMC Nurs 23 , 399 (2024). https://doi.org/10.1186/s12912-024-02076-8

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Towards efficient waste management: identification of waste flow chains in micro-regional detail through monitored data

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• Lucie Němcová 1 ,
• Jaroslav Pluskal 1 ,
• Radovan Šomplák 1 &
• Jakub Kůdela 2  

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Countries around the world are gradually implementing the transition to a circular economy in waste management. This effort should be initiated already at the waste producers. It is necessary to plan and monitor waste management in as much detail as possible, i.e. at the level of micro-regions. At present, only indicators at the national level are analysed, as more detailed data at the micro-regional level are often not available or are burdened with significant errors and inconsistencies. The calculation of waste management indicators for micro-regions will allow to identify the potential for increasing material or energy recovery and to plan the necessary infrastructure directly to these locations instead of blanket and often ineffective legislative actions. This paper presents an approach for determining the producer-treatment linkage, i.e., provides information about each produced waste, where it was treated, and in what way. Such information is often not available based on historical waste management data as there are repeated waste transfers and often aggregated within a micro-region. The network flow approach is based on an iterative procedure combining a simulation with multi-criteria optimization. The chosen criteria replicate expert estimates in investigated issue such as minimum flow splitting, and minimum transfer micro-regions. A data reconciliation is performed where the deviation from all simulations is minimized, given that the capacity constraints of nodes and arcs resulting from the database must be satisfied. The approach is tested on a generated sample task to evaluate the precision and time complexity of the developed tool. Finally, the presented approach is applied to address a case study in the Czech Republic, within which it is possible to identify treatment location and methods for waste from individual regions.

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1 Introduction

One of the pivotal issues in developing and applying waste management (WM) strategies are the deficiencies and lack of reliable data. Historical data are useful for developing optimized the waste recycling networks (Wang et al. 2021a , b ), while real-time data are important for dynamic waste collection models (Anagnostopoulos et al. 2015 ). The study (Hannan et al. 2015 ) developed a classification of information and communication technologies in Solid Waste Management (SWM) and provided a summary of SWM systems with target application, methodology and functional domain. One of the key problems they found was the deficiency in enough data and real-time information. In another study (Manfredi & Goralczyk 2013 ), the authors developed macro-level WM indicators for quantification and monitoring of the potential environmental impacts, benefits, and improvements possibilities resulting from WM strategies and policies for different waste treatment chains. They found that the main obstacle to the calculation of these WM indicators were the limitations on the input data, especially in terms of double counting and different level of aggregation between generation and treatment data. In a paper (Kulczycka et al. 2020 ) that focused on the extractive waste in Poland, the authors studied the data available at the European Union (EU) level. These data were presented by the individual EU countries and showed that even within the EU regulations and guidance systems, there were no consistent, complete and integrated reports that monitor the implementation of measures and support for WM.

In the recent review on the future of WM in smart and sustainable cities (Esmaeilian et al. 2018 ), the authors discuss the shortcomings of the current WM practices and proposed a conceptual framework for a centralized WM system. The three interlinked components studied were the infrastructure for the collection of data to throughout the entire lifespan of a product, the set of new business models that relied on these data for the prevention of waste generation, and the smart sensor-based infrastructure for upstream waste separation and on-time collection. For these advancements to take hold, a proper utilization of the emerging Internet of Things (IoT) technologies will be one of the most vital components (Shyam et al. 2017 ). Another study that focused on the improvement of the European Waste Statistics Regulation (Sileryte et al. 2022 ) and showed through data mapping and analysis processes that there are still several limitations in the collection of waste data and a number of gaps in current CE research and data analysis. The study (Kundariya et al. 2021 ) reviewed the current strategies and monitoring tools for WM and highlighted several innovative strategies for achieving smart and efficient WM plans.

To identify the possible CE opportunities for reduction, reuse, and recycling, it is important to understand material flows. Such understanding can be obtained through material flow analysis (MFA) which is an approach for modelling the flow of materials through the anthroposphere (Brunner & Rechberger 2016 ). From the WM perspective MFA is especially relevant in the context of the Life Cycle Analysis (LCA) of a product. This encompasses the study of the flow of a given material from its use in the manufacturing of a certain product, through the use of the product, until the phase when the product (and the material) is considered as waste. The MFA approach was utilized in evaluating the CE transition for plastics in Ireland (Mehta et al. 2022 ), in the development of sustainable E-waste management system in India (Wath et al. 2010 ), in modelling WM scenarios in Serbia (Neskovic Markic et al. 2018 ), and in predictive modelling of household packaging waste flows in Belgium (Kleinhans et al. 2021 ). On a global scale, MFA was utilized to develop WM strategies for the sustainability challenges of plastic waste and products (C. Wang et al. 2021a , b ). The authors also identified four gaps in these existing data for characterizing plastics flows and stocks, including missing data, inconsistent classification, conflicting data, and inexplicit data for plastics waste and products.

There is a high heterogeneity of models applied to the problem of identification of waste generation streams (Beigl et al. 2008 ). Most of these models can be described by four specific criteria: the focused scale (household, region, or country), the type of modelled waste streams (household waste fractions, collection streams, or material streams), the hypothesised independent variables (consumption-related, disposal-related, or production and trade-related), and the modelling method (group comparison, correlation analysis, multiple regression analysis, single regression analysis, input–output analysis, time-series analysis, or system dynamics). The study (Beigl et al. 2008 ) found that in the most cases, correlation and regression analyses, and group comparisons, were the most useful modelling methods. The application of time series analyses and input–output analyses were found preferable for special information needs (for instance in the assessment of seasonal effects for short-term forecasts) or for appropriate data availability. Authors of the study (Kolekar et al. 2016 ) found that the overall size of the household, its level of income, and the level of education were most common attributes that affected waste generation. Johnson et al. ( 2017 ) used 10 years of high-resolution historical refuse and recycling data for waste generation prediction in dense urban area (New York City) across three waste streams of refuse, paper and plastic/glass/metal. Their gradient boosting regression model identified certain regularities of urban waste generation and could even capture fluctuations on a very short timescale, which were associated to special events such as holidays, seasonal variations, and weather related events. Different machine learning models (decision trees and neural networks) were used to model waste generation in Canada (Kannangara et al. 2018 ) based on socio-economic variables (such as fraction of population over 45 years, median personal income, or employment rate) at regional and municipal levels.

The branch of reliable detection, evaluation and adjustment of inconsistencies and errors in WM related data is a very active area of research, both on the theoretical and application sides. In the context of MFA, the study (Laner et al. 2014 ) described the various sources of uncertainty in the MFA results and current practices in handling these issues. They have found that while mathematically simpler concepts that focused on the characterization of data uncertainty were appropriate tools for descriptive MFAs, various statistical approaches enabled a more rigorous evaluation of uncertainty as well as model sensitivity for exploratory MFAs. They also found that irrespective of the sophistication level, the lack of information about the MFA data constituted one of the principal challenges for relevant uncertainty analysis. A freely available software STAN that supports MFA under the consideration of data uncertainties was developed by (Cencic 2016 ). This software is capable of performing nonlinear data reconciliation based on the weighted least-squares approach, and error propagation. A new approach for data uncertainty characterization in MFA was introduced in (Laner et al. 2016 ). The approach consisted of data quality assessment as the basis for uncertainty estimates of input data. The results of a case study (on plastics flows in Austria) indicated that the way of deriving estimates of the uncertainty for material flows had a stronger impact on the ranges of the uncertainty of the resulting flows than the assumptions about the underlying probability distributions. A nonlinear least squares optimization model for the US steel flow was developed by (Zhu et al. 2019 ).

The fundamental idea of data reconciliation is in resolving the imbalances in data by statistically adjusting the collected data based on various assumptions (e.g., that their uncertainty can be described by a certain probability density function) (Narasimhan & Cornelius 1999 ). The study (Cencic & Frühwirth 2015 ) introduced a general framework for data reconciliation of nonnormally distributed data, which was based on Bayesian reasoning. A general nonlinear least squares method based on a nonlinear constrained optimization model was developed in (Kopec et al. 2016 ). Dubois et al. ( 2014 ) proposed a fuzzy set-based approach for data reconciliation. The authors found that this method provided a generalized approach to the choice of estimated values, which was more flexible and less dependent on hard to justify probabilistic assumptions. Authors of the study (Šomplák et al. 2019 ) proposed a multi-objective supply chain-based model for bulky waste streams that were suitable for energy recovery. A mathematical model for forecasting spatially distributed and uncertain data was introduced in (Pavlas et al. 2017 ). A mixed-integer quadratic optimization model for the estimation of the waste amounts that were divert from mixed municipal waste to separate fractions was proposed in (Šomplák et al. 2022 ).

The specific underlying structure of the problem studied in this paper is a network (or a graph) of waste producers and treatment plants. For such problems, there were developed several data reconciliation strategies (Mah et al. 1976 ). A modal decomposition approach for data reconciliation in channel flows was introduced in (Wu et al. 2009 ). In (Oliveira et al. 2015 ), the errors in the data for gas flow measurements were modeled by a multivariate gaussian distribution. A similar modelling choice was used in (Ijaz et al. 2013 ), where the authors also utilized iterative QR factorization in the reconciliation steps in heat exchanger networks. The quadratic programming and unscented Kalman filter approaches for data reconciliation for pipeline networks were compared in (Isom et al. 2018 ). The results of the comparison found that the quadratic programming approach computed quickly with acceptable accuracy, but the Kalman filter one produced more accurate results. Two different nonlinear programming methods, a local iterative one and a global one, for data reconciliation in energy system flowsheets were compared in (Yong et al. 2016 ). Their results showed that the global approach gave better accuracy but the iterative approach was simpler and produced reasonable results when computational complexity was an issue. A network-based data reconciliation model in a hydrogen network of an oil refinery was developed in (Galan et al. 2019 ), where the authors utilized a large-scale nonlinear programming formulation.

2 Novelty of the contribution

The transition to the circular economy is monitored via WM indicators, which are mostly evaluated on the national level. For this purpose, approaches based on MFA are mostly used and they can be considered suitable in the case of clear input information without significant errors. Issues on the national level usually have negligible errors due to data aggregation. However, for a more detailed evaluation of waste indicators at the level of micro-regions, it is necessary to track waste flows from the producer to the treatment node. In general, waste can be generated by both households and businesses. However, within the analyzed data from monitoring, household waste is aggregated and reported by municipality as one value, which can be applied also for small companies. Usually, data on waste flows between entities in the system are available to construct a Sankey diagram, but the originator of the transported waste is not known. Such information is not readily available in databases since all waste is merged into a single category after the first transfer between entities in the system.

An illustrative example can be seen in the Fig.  1 . It shows a problem involving 5 nodes, with 2 producers, 2 treatments, and 1 transfer node. The goal is to determine how the two producers handle waste, and as an example, material recovery is given. The descriptions of the symbols are as follows:

Example of investigated problem

\({P}_{X},{P}_{Y}\) waste production in node X and Y, [t].

\({X}_{1},{Y}_{1}\) waste from X or Y treated in node 1 (material recovery), [t].

\({X}_{2},{Y}_{2}\) waste from X or Y treated in node 1 (landfill), [t].

\({X}_{1}/{P}_{X}\) the sought value of material recovery indicator in node X, [−].

\({Y}_{1}/{P}_{Y}\) the sought value of material recovery indicator in node Y, [−].

As can be seen, the amount of waste transported between individual entities is known, but the information related to the originator of waste transported along black arcs to specific treatment is not available. Therefore, it is not possible to evaluate material recovery indicators for production nodes. Current WM plans often give unacceptable values (e.g. material recovery greater than 100%), which is probably influenced by waste transports between regions. This issue, based on working with real-world data, is currently not adequately addressed and can be considered crucial in order to identify inefficiencies in the system or lack of appropriate treatment infrastructure and overall improve WM efficiency.

This article proposes a mathematical modeling approach to address this real problem. The task is based on repeated simulations, where waste production and treatment are known, but information about waste flows is available only between two directly connected entities. The proposed evaluation, reflecting expertly defined criteria, should help to reveal or estimate entire chains from producer to final treatment node. In addition to the absolute amount of waste, the presented approach can also track the quality or properties of waste (e.g. calorific value, dry matter, hazardous content, etc.), which is usually reported only in its origin, and afterward, such information is lost by subsequent handling and handovers. However, it represents key indicators for planning appropriate technology and capacity. The previous authors' research can be mentioned, where the impact of waste calorific value on a Waste-to-Energy plant was investigated (Pluskal et al. 2022 ). The last work of the authors was focused on reconciling data to ensure mass balances in waste management data, which are directly obtained from entities involved in this system (Pluskal et al. 2023 ). The goal was to ensure data consistency to enable the use of additional approaches for analysis and refinement of estimates on waste management indicators. This paper builds on the previous research activity with the aim of expanding the portfolio of methods with another approach that can significantly contribute to the evaluation of microregional indicators and track waste flows between regions.

This innovative approach can provide a more accurate assessment of waste flows including its content as well as evaluation of WM indicators in the greatest possible detail. The potential of this approach's results lies in identifying opportunities for infrastructure improvements by tracking waste from producer to treatment node together with its properties. The main contribution of this approach lies in providing information and raising questions about why waste from certain areas is more likely to end up in landfills while waste from others is more likely to be used for material recovery. While similar problems can be addressed through allocation problems by designing future processing chains, such solutions may overlook illogical behavior of some actors, pointing to inefficiencies in the system. It is also challenging to observe all market mechanisms in such tasks, potentially missing inefficiencies that arise for specific reasons, which are just included in the historical data. Moreover, allocation tasks assume central planning and optimal design when all actors work together, which may not be realistic in the real world.

3 Mathematical modelling

The aim of the study is to reconstruct the waste flow from its origin to the treatment location from the available data. The input information available from the WM database consists of waste production and treatment in each node and the amount of waste transferred along each arc connecting two directly connected nodes. Waste production is characterized based on its production into three types: A, B, and C. Primary waste production is classified as type A production. Secondary production and foreign imports are classified as type B production and type C production refers to waste production from the prior year. In the case of production of type A or type C the first transfer of the waste is known, these transfers will be referred to as transfers of type A or type C. However, the originator of waste in subsequent handling is noised by waste from other producers and it is subject of the proposed evaluation. In the case of type B production, there is no known transfer. Therefore, the identification of the whole chain from producer to treatment node is the subject of the proposed evaluation.

Based on an expert opinion, the waste flow follows three principles: transport over the shortest distance, minimizing the number of waste transfers, and minimizing the number of treatment locations. Other criteria specific to the selected waste type may be included and they can significantly contribute to flow identification if the added information describes decision-making in waste handling. However, the aim of this paper is to present rules generally applied for each waste type. The waste flow problem can be transformed into a directed graph problem, where the nodes represent the considered territorial units and the directed arcs represent the waste transport between them. The optimization algorithm for flow reconstruction was developed with respect to maintaining the balance in the flow networks. Since mixed-integer programming is used and it is intended to be used on real (and, therefore, extensive) data, time and accuracy issues may arise. Therefore, the chosen algorithm performs the calculation only for a subset of the nodes. To ensure the relevance of the solution, the calculation is repeated several times with different orders of solved nodes (randomly generated permutations), resulting in different scenarios that are evaluated by the final model. First, the relevant sets, indices, parameters, and variables are introduced.

The algorithm consists of an inner cycle in which \(\text{q}\) waste flows are optimized at a time. After each run, the arc and treatment capacities are updated based on the solution. These steps are repeated until all flows are solved. This procedure is repeated \(\text{R}\) times with different orders of optimized flows. This results in \(\text{R}\) scenarios that the final model evaluates into a single solution. Algorithm 1 describes the pseudocode of the algorithm used for flow reconstruction.

Pseudocode of the proposed algorithm

The following text focuses on the description of the sub-models that are used to evaluate flows from selected nodes in a given order according to the generated permutation. Sub-models 1, 2, and 3 optimize the flow based on solely one of the mentioned principles, while the sub-model 4 combines them.

3.1 Sub-model 1

Sub-model 1 minimizes the waste transport distance in which the exact arc length is replaced by the arc cost given by the function \({\text{g}(\text{d}}_{\text{j}})\) that divides the arcs into short, medium, and long. This approach was chosen because it reflects reality more accurately according to the study in the field of logistics (Gregor et al. 2017 ). The constraints defined by Eqs. ( 2 and 3 ) ensure that the flow network balance in the nodes is maintained. The upper boundaries for flow along the arcs are given by Eqs. ( 4 and 5 ) and the lower ones by Eq. ( 6 ). Equation ( 7 ) describes the maximum amount of waste treated at the nodes. And lastly, the relation between variables \({\text{x}}_{\text{ipj}}\) and \({\updelta }_{\text{ij}}\) is given by Eq. ( 8 ).

The second term in the objective function represents penalization of relaxation of the constraints given by Eqs. ( 4 , 5 and 7 ). Since the sub-model is solved iteratively, it may happen that due to previous optimization and related reduction of transport and treatment capacities, the sub-model becomes infeasible. The relaxation variables \({\text{e}}_{\text{j}}\) and \({\text{E}}_{\text{k}}\) enable the capacity constraints to be relaxed under a large penalty \(\text{V}\) , thereby eliminate the infeasibilities. This term is required to be present in the objective function of each sub-model to ensure that relaxation occurs only when the sub-model would be infeasible without it.

3.2 Sub-model 2

The second sub-model focuses solely on minimizing the number of waste transfer points. It builds upon the framework of model 1 with modified objective function Eq. ( 1 ), in which the function \({\text{f}}_{1}\) is replaced with function \({\text{f}}_{2}\) as follows:

All the constraints (2–8) remain unchanged.

3.3 Sub-model 3

Sub-model 3 aims to minimize the number of waste treatment locations, which is reflected in its objective function, where the function \({\text{f}}_{1}\) is replaced by the function \({\text{f}}_{3}\) , which prioritizes the reduction of treatment locations:

The constraints (2)-(7) are kept, with addition of the constraint Eq. ( 11 ) describing the relation between \({\text{X}}_{\text{ipk}}\) and \({\upgamma }_{\text{ik}}\) :

3.4 Sub-model 4

Sub-model 4 is a multi-objective model, which combines all three principles mentioned above with approximately equal importance. That is achieved by solving the sub-models 1, 2 and 3 first and using their optimal objective values to calculate the weights.

To steps are as follows:

Solve sub-model 1, get \({\text{f}}_{1}^{*}=\text{optimal value of }\text{the }{\text{function}} {\text{f}}_{1}\)

Solve sub-model 2, get \({\text{f}}_{2}^{*}=\text{optimal value of }\text{the }{\text{function}} {\text{f}}_{2}\)

Solve sub-model 3, get \({\text{f}}_{3}^{*}=\text{optimal value of }\text{the }{\text{function}} {\text{f}}_{3}\)

Solve sub-model 4, where the objective function is defined by following Eq. ( 12 )

3.5 Final model

To evaluate the solutions generated by a sub-model for each scenario, a final model is introduced. The scenario solutions are treated as result candidates and the final model looks for a suitable solution, which is the closest to the median value, since that was the most frequent result of the sub-model. In addition, the final model must respect the constraints that arise from the nature of the task, such as mass balance in nodes. This can be represented mathematically by a model that minimizes the deviation from the scenario results using the taxicab metric. The metric makes the objective function nonlinear, but it can be linearized by introducing substitution variables \({\Delta }_{\text{ipj}}\) and constraints Eq. ( 16 ), ( 17 ).

4 Simulation and evaluation

The lack of data about waste flows from producer to treatment location makes it difficult to assess the proposed method. To overcome this issue, waste flow can be simulated based on the existing data. Simulating waste flow can provide insights into the impact of using different sub-models and identify areas for improvement.

In order to generate a problem that would reflect the real case, the real data were first explored. In this case, real data from WM practices in the Czech Republic was used. For each waste type with available data, several characteristics have been determined. Waste types managed in less than 50 facilities were excluded from the analysis because they represent special cases.

The first characteristic that was investigated was an average ratio of the number of producers to the number of treatment facilities. A histogram of this ratio can be seen in Fig.  2 a. Outliers were removed from the histogram, including the maximum value which was 314. The outliers represent extreme cases in which the number of producers is significantly higher than the number of treatment facilities. These cases highly influenced the mean value, which is why the median value is a better representation of this ratio. The median value is approximately 2.3, which means that there are approximately twice as many waste producers as waste treatment facilities.

Histograms of key characteristics of waste management

In Fig.  2 b there is shown a histogram of the average ratio of the number of transfers to the number of producers. Both median and mean values are approximately 1.2. Mean value close to one signifies that in most cases the waste was sent directly from the producer to the treatment facility. Cases where the ratio is less than one indicates that there were more occasions when producers directly treated the waste. On the other hand, values larger than one mean that there were multiple transfers between the producer and the treatment facility. Generally, this ratio tends to be no greater than 2. This indicates that the waste is transferred at most through one collection facility in the majority of cases.

The average ratio of the number of transfers of type A and type C to the total number of transfers is displayed in a histogram in Fig.  2 c. The mean and median values, both around 0.8, signify that the first transfers of type A and type C waste collectively constitute approximately 80% of all transfers. Thus, only about 20% of all transfers are of waste of unknown origin. These are either subsequent transfers of waste of type A or C or any transfers of type B waste. This is consistent with the aforementioned observation that in majority of cases multiple transfers do not occur. In addition, transfers of waste of type B were found to be significantly less frequent.

Figure  2 d shows a histogram of the average length of transfers. The lengths of transfers take values between 11 and 194 km with the mean value around 70.8 km. This shows that, in practice, many long transfers of more than 100 km are made.

The last two characteristics examined describe the differences in the amount of waste produced and treated in the nodes. First, the average amount of produced waste was calculated as the total waste production divided by the number of the producers. Individual producers were considered as small producers if they produced less than the average amount of waste. In the opposite case they were considered as large producers. Based on this rule the average ratio of the number of small producers to the number of large producers was calculated. Similarly, the average ratio of the number of small treatment facilities to the number of large treatment facilities was determined. The histograms of these ratios are shown in Fig.  2 e,f. Both histograms had to be adjusted due to several outliers which skewed the mean value. The median value provided in Fig.  2 e denotes that there are 5 times more small producers than the large ones. In the case of treatment facilities on average, there are 4 times more small treatment facilities.

A test problem is generated based on the key parameters pertaining to network complexity which are represented by their median values reported in the histograms in Fig.  2 . The initial phase involves the generation of a designated number of nodes, assigning each node a random position within the defined space and labelling them based on size and whether they function as processing nodes. Subsequently, a production is generated in each node according to the corresponding type, which is then randomly transferred to another node. The waste transfer is repeated with a reduced probability so that the ratio between known and unknown transfers is approximately balanced. The last transfer is then performed to a treatment node. The selection of a node for waste transfer is made based on the probability determined by the distance in order to prefer closer nodes. In this manner, the waste production is resolved in each node so that the restriction on the number of nodes and the corresponding production is met.

To evaluate the method, it is essential to define an appropriate criterion against which the validity can be assessed. An analogy to 0–1 loss function was chosen as a convenient criterion:

where \(\widehat{\text{X}}\) is a vector of obtained optimal results of variable \(\text{X}\) and \({\text{X}}^{*}\) is the corresponding vector of the true results. Criterion L serves as an indicator of the accuracy with which the final model identifies the treatment locations.

Treatment of waste is confined exclusively to nodes with treatment facilities. As the median of the average ratio of the number of producers to the number of treatment facilities suggests, such nodes constitute less than half of the total nodes in the flow network. The models reflect this fact by constraining the treatment capacity, resulting in a sparse matrix \(\widehat{\text{X}}\) . The sparsity also arises from the infrequent separation of flows and, consequently, the concentration of waste treatment in a low number of treatment facilities. This inherent sparsity contributes to a high value of L, consistently exceeding 99%. To address this, the application of Criterion L has been restricted exclusively to indices corresponding to positive values of \({\text{X}}_{\text{ipk}}^{*}\) ​, signifying actual occurrences of waste treatment. This targeted approach ensures that the criterion is applied only in instances where waste treatment is actively taking place.

The waste flow reconstruction algorithm was tested on simulated problems to assess time complexity and accuracy via Criterion L across varying problem sizes. The considered sizes of the networks ranged from 50 to 1000 nodes. The number of optimized nodes q was fixed at 50 while the number of scenarios was set at 10 for problems up to 500 nodes. For larger tasks only 5 scenarios were considered due to significant increase in time complexity. To capture possible ambiguous solutions the calculations were repeated for all network sizes, with the number of simulated tasks corresponding with the number of scenarios. The testing was performed using the sub-model 4. This sub-model was selected for its robustness and closest resemblance with real-world dynamics, as it combines all three principles.

Figure  3 illustrates the relation between average elapsed time per scenario in minutes and the total number of nodes in the network through the representation of red points. The red line in Fig.  3 corresponds to an exponential function fitted to these data points using non-linear least squares method. The fitted line reveals an exponential increase in computation time for larger networks. Moreover, it should be taken into account that it is recommended to calculate more than one scenario, in such cases the reconstruction of flows for large networks can take several hours. Consequently, this approach is deemed less suitable for addressing problems characterized by more than 1000 nodes. Conversely, for smaller-scale problems, the time complexity remains within acceptable bounds.

Time per scenario [min] and Criterion L [%] dependency on number of nodes in the network

In Fig.  3 , the relation between the accuracy of the method, as measured by Criterion L, and the size of the problem is depicted by a blue line. This line corresponds to a logarithmic function that was fitted to the actual values of L, represented by the blue points. It can be noticed that there is a slight decrease in accuracy with increasing problem size. Despite the observed decrease in accuracy with larger tasks, it should be noted that the average L value for tasks involving 1000 nodes was 81%, which indicates an acceptable level of accuracy. This result highlights the robustness of the method and its suitability for solving problems of different sizes without compromising the overall effectiveness of the approach.

5 Case study

A case study was conducted using real data pertaining to waste management in the Czech Republic, focusing specifically on waste identified in waste catalogue by code 19 12 12 (SEPA 2015 ). This category includes various wastes generated in the process of mechanical treatment of waste, which may include processes such as sorting, shredding or grinding. Examples of wastes that could fall into this category are specific to the context and the processes that are carried out. This can include different materials or mixtures of materials (most commonly paper and plastic) that are sorted or processed mechanically. In most cases, these wastes are generated in recycling facilities or waste treatment facilities. This is the other non-hazardous waste that is no longer a significant component in terms of further recycling processes (technologically non-recyclable material) but can be made into fuel for energy recovery. The unusable components are mostly landfilled. The waste management system associated with this particular waste type comprises a network of 677 nodes, with 478 nodes involved in waste production and 207 nodes dedicated to waste treatment. Given the inherent errors and inconsistencies in the real data, a revision and reconciliation process were undertaken using a model detailed in (Pluskal et al. 2023 ). During this process it was found that 13% of the waste was processed within the production node, so the producer-treatment chain was completely known and only remaining waste was included in further analysis.

The method proposed in this study was applied to the reconciled data considering 10 scenarios and optimizing the sub-model simultaneously for 50 flows. The computational process required approximately two and a half hours, exceeding the originally estimated time shown in Fig.  3 . This increase in duration can be attributed to the higher number of transfers, which totalled 957. This means that the ratio of transfers to producers is approximately 2, which exceeds the median value provided in Fig.  2 b.

Figure  4 shows a histogram of flows based on the direct distances between production nodes and treatment facilities. The amount of waste treated by distance showed a similar trend as the histogram in Fig.  4 . The median value of direct distance between producer and treatment facility was 41 km, that means that the majority of waste is treated within short distances. More specifically, 72% of the total quantity is treated within 100 km distance. However, certain flows are implemented over very long distances across the country. Consequently, presenting a comprehensive visualization of all flows between producers and treatment facilities on a map would result in a lack of clarity. Hence, Fig.  5 selectively illustrates on the map only those flows that have undergone treatment in three specifically chosen nodes. Despite this limitation, the depicted figure reveals a distinct flow pattern. It indicates that a significant portion of the treated waste at the facility originates from proximate sources while acknowledging to a lesser extent the import of waste from more distant locations.

Histogram of flows based on the direct distances between producers and treatment facilities

Map of the Czech Republic with indicated waste flows ending at three selected nodes

The proposed method enabled the complete determination of the entire producer-treatment chain. This was essential to identify the specific treatment methods used.

In this study, the proportional relation between the treatment methods employed at the treatment facility and those applied to the waste treated within that facility was taken into consideration.

However, it is important to note that certain assumptions about treatment methods provided by experts may have been applied to the analysis. Consequently, this approach enables the calculation of key indicators not only for waste treatment facilities but, more significantly, for producers across diverse territorial divisions. Figure  6 serves as an illustrative representation, presenting a map of the Czech Republic accompanied by pie charts that show the used waste treatment methods corresponding to the produced waste in each region. This visualization offers a valuable insight into the character of waste management practices at the regional level.

A map of the Czech Republic with pie charts of treatment methods of waste from for each region

Evident distinctions in both production (as indicated by colour depth) and treatment methods exist among the regions. In particular, the Central Bohemian Region is the main producer of this type of waste. Approximately half of its production is used for energy and material recovery or to pre-treatment (which denotes an exchange of wastes for submission to any of the permissioned operations), methods, those are the methods which are recognised for their sustainability. In contrast, the Moravian-Silesian Region, the second largest producer, relies predominantly on landfilling as a method of waste disposal, a practice considered undesirable and suggesting potential for improvement. This approach makes it possible to carry out a detailed analysis of each region, or even micro-regions, and to identify specific areas for improvement. This knowledge can be considered particularly valuable, for instance in the context of planning the expansion of waste management infrastructure.

Exploring the dependence of waste treatment methods on transport distance also provided valuable insights. In this context, the transport distance is quantified as the direct distance between producers and treatment facilities. The categorization of flows into short transport distances (up to 100 km) and long transport distances (above 100 km) reveals a noticeable variation in treatment methods, as depicted in Fig.  7 . In particular, material recovery along with pre-treatment emerge as significantly more prevalent waste treatment methods for waste transported over longer distances. Conversely, waste transported over shorter distances is more inclined to be disposed of through landfilling. This observed trend suggests that adopting more environmentally friendly treatment methods often involves the trade-off of longer transport distances.

Treatment methods based on transport distance

6 Conclusion

The main objective of the article is to identify the complete waste flow chains from the producer to the treatment node, while the partial flows are known only between two entities without knowing of subsequent handovers. This information about the whole chain with waste quality or properties can be beneficial for WM monitoring, as it helps in identifying potential micro-regions and areas for improving efficiency, establishing the necessary infrastructure, and contributing to the transition to a circular economy. Without these data, evaluating the monitored WM criteria in greater detail is not possible beyond the state level, and therefore, instead of a blanket and often ineffective legislative interventions, actions cannot be targeted to specific micro-region conditions. The developed approach allows for a more comprehensive subsequent analysis of WM than before, enabling better decision-making and resource allocation. This, in turn, can lead to significant economic, social, and environmental benefits in the long term.

The identification of these waste flows is carried out through flow optimization in the network and a gradual simulation calculation, which results in scenarios of possible locations and treatment types. The result is selected as the one that is least distant from all others. The proposed models were based on three principles: minimal transport distance, minimal number of transfers, and minimal number of treatment locations. Other criteria can bring more accurate identification of chains if they describe the behavior of handling specific waste type. The presented paper aims to rules, that can be applied generally. The approach was tested on generated tasks reflecting key characteristics of real evidence of different scales. While the time complexity increased exponentially, the accuracy of the method decreased marginally. A case study on residues from mechanical treatment (code 19 12 12) in the Czech Republic has been conducted. The study revealed significant differences in the treatment methods used to treat waste from different regions. Moreover, it was found that waste that was transported over longer distances was more often treated in more sustainable ways, such as material recovery.

In future research, a crucial focus will be on addressing potential time complexity issues. Implementing optimization by parts, followed by merging obtained results, emerges as a promising solution, especially in the case of more important waste streams containing thousands of nodes. Additionally, the assignment of treatment methods will be explored further, employing alternative approaches such as principles based on expert opinion. An integral aspect of this research will involve exploring differences in indicators calculated for the location of treatment facilities versus the location of waste production. This comprehensive approach aims to provide nuanced insights into the environmental impacts across the entire life cycle of waste management processes, contributing to the development of more effective, personalized, and context-specific strategies in the field.

Data availability

The codes used to run the experiments reported in the paper can be found in the supplementary material.

Abbreviations

European Union

Internet of things

Life cycle assessment

Material flow analysis

Solid waste management

Waste management

Anagnostopoulos T, Kolomvatsos K, Anagnostopoulos C, Zaslavsky A, Hadjiefthymiades S (2015) Assessing dynamic models for high priority waste collection in smart cities. J Syst Softw 110:178–192. https://doi.org/10.1016/j.jss.2015.08.049

Article   Google Scholar  

Beigl P, Lebersorger S, Salhofer S (2008) Modelling municipal solid waste generation: a review. Waste Manage 28:200–214. https://doi.org/10.1016/j.wasman.2006.12.011

Brunner PH, Rechberger H (2016) Handbook of material flow analysis. CRC Press. https://doi.org/10.1201/9781315313450

Cencic O (2016) Nonlinear data reconciliation in material flow analysis with software STAN. Sustain Environ Res 26:291–298. https://doi.org/10.1016/j.serj.2016.06.002

Cencic O, Frühwirth R (2015) A general framework for data reconciliation—Part I: linear constraints. Comput Chem Eng 75:196–208. https://doi.org/10.1016/j.compchemeng.2014.12.004

de Oliveira EC, Nogueira Frota M, de Oliveira Barreto G (2015) Use of data reconciliation: a strategy for improving the accuracy in gas flow measurements. J Nat Gas Sci Eng 22:313–320. https://doi.org/10.1016/J.JNGSE.2014.12.008

Dubois D, Fargier H, Ababou M, Guyonnet D (2014) A fuzzy constraint-based approach to data reconciliation in material flow analysis. Int J Gen Syst 43:787–809. https://doi.org/10.1080/03081079.2014.920840

Esmaeilian B, Wang B, Lewis K, Duarte F, Ratti C, Behdad S (2018) The future of waste management in smart and sustainable cities: a review and concept paper. Waste Manage 81:177–195. https://doi.org/10.1016/j.wasman.2018.09.047

Galan A, de Prada C, Gutierrez G, Sarabia D, Grossmann IE, Gonzalez R (2019) Implementation of RTO in a large hydrogen network considering uncertainty. Optim Eng 20:1161–1190. https://doi.org/10.1007/S11081-019-09444-3/FIGURES/13

Article   MathSciNet   Google Scholar  

Gregor J, Šomplák R, Pavlas M (2017) Transportation cost as an integral part of supply chain optimisation in the field of waste management. Chem Eng Trans 56:1927–1932. https://doi.org/10.3303/CET1756322

Hannan MA, Abdulla Al Mamun Md, Hussain A, Basri H, Begum RA (2015) A review on technologies and their usage in solid waste monitoring and management systems: Issues and challenges. Waste Manage 43:509–523. https://doi.org/10.1016/j.wasman.2015.05.033

Ijaz H, Ati UMK, Mahalec V (2013) Heat exchanger network simulation, data reconciliation & optimization. Appl Therm Eng 52:328–335. https://doi.org/10.1016/J.APPLTHERMALENG.2012.11.033

Isom JD, Stamps AT, Esmaili A, Mancilla C (2018) Two methods of data reconciliation for pipeline networks. Comput Chem Eng 115:487–503. https://doi.org/10.1016/J.COMPCHEMENG.2018.05.008

Johnson NE, Ianiuk O, Cazap D, Liu L, Starobin D, Dobler G, Ghandehari M (2017) Patterns of waste generation: a gradient boosting model for short-term waste prediction in New York City. Waste Manage 62:3–11. https://doi.org/10.1016/j.wasman.2017.01.037

Kannangara M, Dua R, Ahmadi L, Bensebaa F (2018) Modeling and prediction of regional municipal solid waste generation and diversion in Canada using machine learning approaches. Waste Manage 74:3–15. https://doi.org/10.1016/j.wasman.2017.11.057

Kleinhans K, Hallemans M, Huysveld S, Thomassen G, Ragaert K, Van Geem KM, Roosen M, Mys N, Dewulf J, De Meester S (2021) Development and application of a predictive modelling approach for household packaging waste flows in sorting facilities. Waste Manage 120:290–302. https://doi.org/10.1016/j.wasman.2020.11.056

Kolekar KA, Hazra T, Chakrabarty SN (2016) A review on prediction of municipal solid waste generation models. Procedia Environ Sci 35:238–244. https://doi.org/10.1016/j.proenv.2016.07.087

Kopec GM, Allwood JM, Cullen JM, Ralph D (2016) A general nonlinear least squares data reconciliation and estimation method for material flow analysis. J Ind Ecol 20:1038–1049. https://doi.org/10.1111/jiec.12344

Kulczycka J, Dziobek E, Szmiłyk A (2020) Challenges in the management of data on extractive waste—the Polish case. Miner Econ 33:341–347. https://doi.org/10.1007/s13563-019-00203-5

Kundariya N, Mohanty SS, Varjani S, Ngo HH, Wong JW, Taherzadeh MJ, Chang JS, Ng HY, Kim SH, Bui XT (2021) A review on integrated approaches for municipal solid waste for environmental and economical relevance: monitoring tools, technologies, and strategic innovations. Bioresource Technol 342:125982

Laner D, Rechberger H, Astrup T (2014) Systematic evaluation of uncertainty in material flow analysis. J Ind Ecol 18:859–870. https://doi.org/10.1111/jiec.12143

Laner D, Feketitsch J, Rechberger H, Fellner J (2016) A novel approach to characterize data uncertainty in material flow analysis and its application to plastics flows in Austria. J Ind Ecol 20(5):1050–1063. https://doi.org/10.1111/jiec.12326

Mah RS, Stanley GM, Downing DM (1976) Reconcillation and rectification of process flow and inventory data. Ind Eng Chem Process Des Dev 15:175–183. https://doi.org/10.1021/I260057A030/ASSET/I260057A030.FP.PNG_V03

Manfredi S, Goralczyk M (2013) Life cycle indicators for monitoring the environmental performance of European waste management. Resour Conserv Recycl 81:8–16. https://doi.org/10.1016/j.resconrec.2013.09.004

Markic DN, Carapina HS, Bjelic D, Bjelic LS, Ilic P, Pesic ZS, Kikanovicz O (2019) Using material flow analysis for waste management planning. Polish J Environ Studies 28(1):255–265. https://doi.org/10.15244/pjoes/78621

Mehta N, Cunningham E, Doherty M, Sainsbury P, Bolaji I, Firoozi-Nejad B, Smyth BM (2022) Using regional material flow analysis and geospatial mapping to support the transition to a circular economy for plastics. Resour Conserv Recycl 179:106085. https://doi.org/10.1016/j.resconrec.2021.106085

Narasimhan S, Cornelius J (1999) Data Reconciliation and gross error detection. Data Reconciliation and Gross Error Detection. https://doi.org/10.1016/B978-0-88415-255-2.X5000-9

Pavlas M, Šomplák R, Smejkalová V, Nevrlý V, Zavíralová L, Kůdela J, Popela P (2017) Spatially distributed production data for supply chain models - Forecasting with hazardous waste. J Clean Prod 161:1317–1328. https://doi.org/10.1016/j.jclepro.2017.06.107

Pluskal J, Šomplák R, Hrabec D, Nevrlý V, Hvattum LM (2022) Optimal location and operation of waste-to-energy plants when future waste composition is uncertain. Oper Res Int Journal 22:5765–5790. https://doi.org/10.1007/s12351-022-00718-w

Pluskal J, Šomplák R, Němcová L, Valta J, Pavlas M (2023) Mathematical modelling of waste flows and treatment based on reconstruction of historical data: case of wastewater sludge in Czech Republic. J Clean Prod 420:138393. https://doi.org/10.1016/j.jclepro.2023.138393

SEPA (2015) Guidance on using the European Waste Catalogue (EWC) to code waste. Scottish Environment Protection Agency. https://www.sepa.org.uk/media/163421/ewc_guidance.pdf . Accessed 26 November 2023

Shyam GK, Manvi SS, Bharti P (2017) Smart waste management using Internet-of-Things (IoT). In: 2017 2nd International Conference on Computing and Communications Technologies (ICCCT) 199–203. https://doi.org/10.1109/ICCCT2.2017.7972276

Sileryte R, Sabbe A, Bouzas V, Meister K, Wandl A, van Timmeren A (2022) European waste statistics data for a circular economy monitor: opportunities and limitations from the Amsterdam metropolitan region. J Clean Prod 358:131767. https://doi.org/10.1016/j.jclepro.2022.131767

Šomplák R, Nevrlý V, Smejkalová V, Šmídová Z, Pavlas M (2019) Bulky waste for energy recovery: analysis of spatial distribution. Energy 181:827–839. https://doi.org/10.1016/j.energy.2019.05.175

Šomplák R, Smejkalová V, Kůdela J (2022) Mixed-integer quadratic optimization for waste flow quantification. Optim Eng 23:2177–2201. https://doi.org/10.1007/s11081-022-09762-z

Wang C, Liu Y, Chen W, Zhu B, Qu S, Xu M (2021a) Critical review of global plastics stock and flow data. J Ind Ecol 25:1300–1317. https://doi.org/10.1111/jiec.13125

Wang Z, Huang L, He CX (2021b) A multi-objective and multi-period optimization model for urban healthcare waste’s reverse logistics network design. J Comb Optim 42(4):785–812. https://doi.org/10.1007/s10878-019-00499-7

Wath SB, Vaidya AN, Dutt PS, Chakrabarti T (2010) A roadmap for development of sustainable E-waste management system in India. Sci Total Environ 409:19–32. https://doi.org/10.1016/j.scitotenv.2010.09.030

Wu Q, Litrico X, Bayen AM (2009) Data reconciliation of an open channel flow network using modal decomposition. Adv Water Resour 32:193–204. https://doi.org/10.1016/J.ADVWATRES.2008.10.009

Yong JY, Nemet A, Bogataj M, Zore Ž, Varbanov PS, Kravanja Z, Klemeš JJ (2016) Data reconciliation for energy system flowsheets. Comput Aided Chem Eng 38:2277–2282. https://doi.org/10.1016/B978-0-444-63428-3.50384-2

Zhu Y, Syndergaard K, Cooper DR (2019) Mapping the annual flow of steel in the United States. Environ Sci Technol 53:11260–11268. https://doi.org/10.1021/acs.est.9b01016

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Open access publishing supported by the National Technical Library in Prague. The authors gratefully acknowledge the financial support provided by the Technology Agency of the Czech Republic, grant No. SS02030008 “Centre of Environmental Research: Waste management, circular economy and environmental security”.

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Conceptualization, L.N., J.P., R.Š. and J.K.; methodology, L.N. and J.K.; investigation, L.N. and J.P.; resources, J.P. and R.Š.; data curation, L.N., J.P. and R.Š.; writing—original draft preparation, L.N., J.P. and R.Š.; writing—review and editing, L.N., J.P. and J.K.; visualization, L.N. and J.P.; supervision, R.Š and J.K.; project administration, R.Š.; funding acquisition, R.Š. All authors read and approved the final manuscript.

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Němcová, L., Pluskal, J., Šomplák, R. et al. Towards efficient waste management: identification of waste flow chains in micro-regional detail through monitored data. Optim Eng (2024). https://doi.org/10.1007/s11081-024-09897-1

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DOI : https://doi.org/10.1007/s11081-024-09897-1

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