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The Importance of Research Design: A Comprehensive Guide

Research design plays a crucial role in conducting scientific studies and gaining meaningful insights. A well-designed research enhances the validity and reliability of the findings and allows for the replication of studies by other researchers. This comprehensive guide will provide an in-depth understanding of research design, its key components, different types, and its role in scientific inquiry. Furthermore, it will discuss the necessary steps in developing a research design and highlight some of the challenges that researchers commonly face.

Table of Contents

Understanding research design.

Research design refers to the overall plan or strategy that outlines how a study is conducted. It serves as a blueprint for researchers, guiding them in their investigation, and helps ensure that the study objectives are met. Understanding research design is essential for researchers to effectively gather and analyze data to answer research questions.

When embarking on a research study, researchers must carefully consider the design they will use. The design determines the structure of the study, including the research questions, data collection methods, and analysis techniques. It provides clarity on how the study will be conducted and helps researchers determine the best approach to achieve their research objectives. A well-designed study increases the chances of obtaining valid and reliable results.

Definition and Purpose of Research Design

Research design is the framework that outlines the structure of a study, including the research questions, data collection methods, and analysis techniques. It provides a systematic approach to conducting research and ensures that all aspects of the study are carefully planned and executed.

The purpose of research design is to provide a clear roadmap for researchers to follow. It helps them define the research questions they want to answer and identify the variables they will study. By clearly defining the purpose of the study, researchers can ensure that their research design aligns with their objectives.

Key Components of Research Design

A research design consists of several key components that influence the study’s validity and reliability. These components include the research questions, variables and operational definitions, sampling techniques, data collection methods, and statistical analysis procedures.

The research questions are the foundation of any study. They guide the entire research process and help researchers focus their efforts. By formulating clear and concise research questions, researchers can ensure that their study addresses the specific issues they want to investigate.

Variables and operational definitions are also crucial components of research design. Variables are the concepts or phenomena that researchers want to measure or study. Operational definitions provide a clear and specific description of how these variables will be measured or observed. By clearly defining variables and their operational definitions, researchers can ensure that their study is consistent and replicable.

Sampling techniques play a vital role in research design as well. Researchers must carefully select the participants or samples they will study to ensure that their findings are generalizable to the larger population. Different sampling techniques, such as random sampling or purposive sampling, can be used depending on the research objectives and constraints.

Data collection methods are another important component of research design. Researchers must decide how they will collect data, whether through surveys, interviews, observations, or experiments. The choice of data collection method depends on the research questions and the type of data needed to answer them.

Finally, statistical analysis procedures are used to analyze the collected data and draw meaningful conclusions. Researchers must determine the appropriate statistical tests or techniques to use based on the nature of their data and research questions. The choice of statistical analysis procedures ensures that the data is analyzed accurately and that the results are valid and reliable.

Types of Research Design

Research design encompasses various types that researchers can choose depending on their research goals and the nature of the phenomenon being studied. Understanding the different types of research design is essential for researchers to select the most appropriate approach for their study.

When embarking on a research project, researchers must carefully consider the design they will employ. The design chosen will shape the entire study, from the data collection process to the analysis and interpretation of results. Let’s explore some of the most common types of research design in more detail.

Experimental Design

Experimental design involves manipulating one or more variables to observe their effect on the dependent variable. This type of design allows researchers to establish cause-and-effect relationships between variables by controlling for extraneous factors. Experimental design often relies on random assignment and control groups to minimize biases.

Imagine a group of researchers interested in studying the effects of a new teaching method on student performance. They could randomly assign students to two groups: one group would receive instruction using the new teaching method, while the other group would receive instruction using the traditional method. By comparing the performance of the two groups, the researchers can determine whether the new teaching method has a significant impact on student learning.

Experimental design provides a strong foundation for making causal claims, as it allows researchers to control for confounding variables and isolate the effects of the independent variable. However, it may not always be feasible or ethical to manipulate variables, leading researchers to explore alternative designs.

Free 44-page Experimental Design Guide

For Beginners and Intermediates

• Introduction to experimental methods
• Respondent management with groups and populations
• How to set up stimulus selection and arrangement

Non-Experimental Design

Non-experimental design is used when it is not feasible or ethical to manipulate variables. This design relies on naturally occurring variations in data and focuses on observing and describing relationships between variables. Non-experimental design can be useful for exploratory research or when studying phenomena that cannot be controlled, such as human behavior.

For instance, researchers interested in studying the relationship between socioeconomic status and health outcomes may collect data from a large sample of individuals and analyze the existing differences. By examining the data, they can determine whether there is a correlation between socioeconomic status and health, without manipulating any variables.

Non-experimental design allows researchers to study real-world phenomena in their natural setting, providing valuable insights into complex social, psychological, and economic processes. However, it is important to note that non-experimental designs cannot establish causality, as there may be other variables at play that influence the observed relationships.

Quasi-Experimental Design

Quasi-experimental design resembles experimental design but lacks the element of random assignment. In situations where random assignment is not possible or practical, researchers can utilize quasi-experimental designs to gather data and make inferences. However, caution must be exercised when drawing causal conclusions from quasi-experimental studies.

Consider a scenario where researchers are interested in studying the effects of a new drug on patient recovery time. They cannot randomly assign patients to receive the drug or a placebo due to ethical considerations. Instead, they can compare the recovery times of patients who voluntarily choose to take the drug with those who do not. While this design allows for data collection and analysis, it is important to acknowledge that other factors, such as patient motivation or severity of illness, may influence the observed outcomes.

Quasi-experimental designs are valuable when experimental designs are not feasible or ethical. They provide an opportunity to explore relationships and gather data in real-world contexts. However, researchers must be cautious when interpreting the results, as causal claims may be limited due to the lack of random assignment.

By understanding the different types of research design, researchers can make informed decisions about the most appropriate approach for their study. Each design offers unique advantages and limitations, and the choice depends on the research question, available resources, and ethical considerations. Regardless of the design chosen, rigorous methodology and careful data analysis are crucial for producing reliable and valid research findings.

The Role of Research Design in Scientific Inquiry

A well-designed research study enhances the validity and reliability of the findings. Research design plays a crucial role in ensuring the scientific rigor of a study and facilitates the replication of studies by other researchers. Understanding the role of research design in scientific inquiry is vital for researchers to conduct impactful and robust research.

Ensuring Validity and Reliability

Research design plays a critical role in ensuring the validity and reliability of the study’s findings. Validity refers to the degree to which the study measures what it intends to measure, while reliability pertains to the consistency and stability of the results. Through careful consideration of the research design, researchers can minimize potential biases and increase the accuracy of their measurements.

Facilitating Replication of Studies

A robust research design allows for the replication of studies by other researchers. Replication plays a vital role in the scientific process as it helps confirm the validity and generalizability of research findings. By clearly documenting the research design, researchers enable others to reproduce the study and validate the results, thereby contributing to the cumulative knowledge in a field.

Steps in Developing a Research Design

Developing a research design involves a systematic process that includes several important steps. Researchers need to carefully consider each step to ensure that their study is well-designed and capable of addressing their research questions effectively.

Identifying Research Questions

The first step in developing a research design is to identify and define the research questions or hypotheses. Researchers need to clearly articulate what they aim to investigate and what specific information they want to gather. Clear research questions provide guidance for the subsequent steps in the research design process.

Selecting Appropriate Design Type

Once the research questions are identified, researchers need to select the most appropriate type of research design. The choice of design depends on various factors, including the research goals, the nature of the research questions, and the available resources. Careful consideration of these factors is crucial to ensure that the chosen design aligns with the study objectives.

Determining Data Collection Methods

After selecting the research design, researchers need to determine the most suitable data collection methods. Depending on the research questions and the type of data required, researchers can utilize a range of methods, such as surveys, interviews, observations, or experiments. The chosen methods should align with the research objectives and allow for the collection of high-quality data.

One of the most important considerations when designing a study in human behavior research is participant recruitment. We have written a comprehensive guide on best practices and pitfalls to be aware of when recruiting participants, which can be read here.

Enhancing Research Design with iMotions and Biosensors

Introduction to enhanced research design.

In the realm of scientific studies, especially within human cognitive-behavioral research, the deployment of advanced technologies such as iMotions software and biosensors has revolutionized research design. This chapter delves into how these technologies can be integrated into various research designs, improving the depth, accuracy, and reliability of scientific inquiries.

Integrating iMotions in Research Design

Imotions software: a key to multimodal data integration.

The iMotions platform stands as a pivotal tool in modern research design. It’s designed to integrate data from a plethora of biosensors, providing a comprehensive analysis of human behavior. This software facilitates the synchronizing of physiological, cognitive, and emotional responses with external stimuli, thus enriching the understanding of human behavior in various contexts.

Biosensors: Gateways to Deeper Insights

Biosensors, including eye trackers, EEG, GSR, ECG, and facial expression analysis tools, provide nuanced insights into the subconscious and conscious aspects of human behavior. These tools help researchers in capturing data that is often unattainable through traditional data collection methods like surveys and interviews.

Application in Different Research Designs

• Eye Tracking : In experimental designs, where the impact of visual stimuli is crucial, eye trackers can reveal how subjects interact with these stimuli, thereby offering insights into cognitive processes and attention.
• EEG : EEG biosensors allow researchers to monitor brain activity in response to controlled experimental manipulations, offering a window into cognitive and emotional responses.

• Facial Expression Analysis : In observational studies, analyzing facial expressions can provide objective data on emotional responses in natural settings, complementing subjective self-reports.
• GSR/EDA : These tools measure physiological arousal in real-life scenarios, giving researchers insights into emotional states without the need for intrusive measures.
• EMG : In studies where direct manipulation isn’t feasible, EMG can indicate subtle responses to stimuli, which might be overlooked in traditional observational methods.
• ECG/PPG : These sensors can be used to understand the impact of various interventions on physiological states such as stress or relaxation.

Streamlining Research Design with iMotions

The iMotions platform offers a streamlined process for integrating various biosensors into a research design. Researchers can easily design experiments, collect multimodal data, and analyze results in a unified interface. This reduces the complexity often associated with handling multiple streams of data and ensures a cohesive and comprehensive research approach.

Integrating iMotions software and biosensors into research design opens new horizons for scientific inquiry. This technology enhances the depth and breadth of data collection, paving the way for more nuanced and comprehensive findings.

Whether in experimental, non-experimental, or quasi-experimental designs, iMotions and biosensors offer invaluable tools for researchers aiming to uncover the intricate layers of human behavior and cognitive processes. The future of research design is undeniably intertwined with the advancements in these technologies, leading to more robust, reliable, and insightful scientific discoveries.

Challenges in Research Design

Research design can present several challenges that researchers need to overcome to conduct reliable and valid studies. Being aware of these challenges is essential for researchers to address them effectively and ensure the integrity of their research.

Ethical Considerations

Research design must adhere to ethical guidelines and principles to protect the rights and well-being of participants. Researchers need to obtain informed consent, ensure participant confidentiality, and minimize potential harm or discomfort. Ethical considerations should be carefully integrated into the research design to promote ethical research practices.

Practical Limitations

Researchers often face practical limitations that may impact the design and execution of their studies. Limited resources, time constraints, access to participants or data, and logistical challenges can pose obstacles during the research process. Researchers need to navigate these limitations and make thoughtful choices to ensure the feasibility and quality of their research.

Research design is a vital aspect of conducting scientific studies. It provides a structured framework for researchers to answer their research questions and obtain reliable and valid results. By understanding the different types of research design and following the necessary steps in developing a research design, researchers can enhance the rigor and impact of their studies.

However, researchers must also be mindful of the challenges they may encounter, such as ethical considerations and practical limitations, and take appropriate measures to address them. Ultimately, a well-designed research study contributes to the advancement of knowledge and promotes evidence-based decision-making in various fields.

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Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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Research Design 101

Everything You Need To Get Started (With Examples)

By: Derek Jansen (MBA) | Reviewers: Eunice Rautenbach (DTech) & Kerryn Warren (PhD) | April 2023

Navigating the world of research can be daunting, especially if you’re a first-time researcher. One concept you’re bound to run into fairly early in your research journey is that of “ research design ”. Here, we’ll guide you through the basics using practical examples , so that you can approach your research with confidence.

Overview: Research Design 101

What is research design.

• Research design types for quantitative studies
• Video explainer : quantitative research design
• Research design types for qualitative studies
• Video explainer : qualitative research design
• How to choose a research design
• Key takeaways

Research design refers to the overall plan, structure or strategy that guides a research project , from its conception to the final data analysis. A good research design serves as the blueprint for how you, as the researcher, will collect and analyse data while ensuring consistency, reliability and validity throughout your study.

Understanding different types of research designs is essential as helps ensure that your approach is suitable  given your research aims, objectives and questions , as well as the resources you have available to you. Without a clear big-picture view of how you’ll design your research, you run the risk of potentially making misaligned choices in terms of your methodology – especially your sampling , data collection and data analysis decisions.

The problem with defining research design…

One of the reasons students struggle with a clear definition of research design is because the term is used very loosely across the internet, and even within academia.

Some sources claim that the three research design types are qualitative, quantitative and mixed methods , which isn’t quite accurate (these just refer to the type of data that you’ll collect and analyse). Other sources state that research design refers to the sum of all your design choices, suggesting it’s more like a research methodology . Others run off on other less common tangents. No wonder there’s confusion!

In this article, we’ll clear up the confusion. We’ll explain the most common research design types for both qualitative and quantitative research projects, whether that is for a full dissertation or thesis, or a smaller research paper or article.

Research Design: Quantitative Studies

Quantitative research involves collecting and analysing data in a numerical form. Broadly speaking, there are four types of quantitative research designs: descriptive , correlational , experimental , and quasi-experimental . 

Descriptive Research Design

As the name suggests, descriptive research design focuses on describing existing conditions, behaviours, or characteristics by systematically gathering information without manipulating any variables. In other words, there is no intervention on the researcher’s part – only data collection.

For example, if you’re studying smartphone addiction among adolescents in your community, you could deploy a survey to a sample of teens asking them to rate their agreement with certain statements that relate to smartphone addiction. The collected data would then provide insight regarding how widespread the issue may be – in other words, it would describe the situation.

The key defining attribute of this type of research design is that it purely describes the situation . In other words, descriptive research design does not explore potential relationships between different variables or the causes that may underlie those relationships. Therefore, descriptive research is useful for generating insight into a research problem by describing its characteristics . By doing so, it can provide valuable insights and is often used as a precursor to other research design types.

Correlational Research Design

Correlational design is a popular choice for researchers aiming to identify and measure the relationship between two or more variables without manipulating them . In other words, this type of research design is useful when you want to know whether a change in one thing tends to be accompanied by a change in another thing.

For example, if you wanted to explore the relationship between exercise frequency and overall health, you could use a correlational design to help you achieve this. In this case, you might gather data on participants’ exercise habits, as well as records of their health indicators like blood pressure, heart rate, or body mass index. Thereafter, you’d use a statistical test to assess whether there’s a relationship between the two variables (exercise frequency and health).

As you can see, correlational research design is useful when you want to explore potential relationships between variables that cannot be manipulated or controlled for ethical, practical, or logistical reasons. It is particularly helpful in terms of developing predictions , and given that it doesn’t involve the manipulation of variables, it can be implemented at a large scale more easily than experimental designs (which will look at next).

That said, it’s important to keep in mind that correlational research design has limitations – most notably that it cannot be used to establish causality . In other words, correlation does not equal causation . To establish causality, you’ll need to move into the realm of experimental design, coming up next…

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Experimental Research Design

Experimental research design is used to determine if there is a causal relationship between two or more variables . With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (dependent variables). Doing so allows you to observe the effect of the former on the latter and draw conclusions about potential causality.

For example, if you wanted to measure if/how different types of fertiliser affect plant growth, you could set up several groups of plants, with each group receiving a different type of fertiliser, as well as one with no fertiliser at all. You could then measure how much each plant group grew (on average) over time and compare the results from the different groups to see which fertiliser was most effective.

Overall, experimental research design provides researchers with a powerful way to identify and measure causal relationships (and the direction of causality) between variables. However, developing a rigorous experimental design can be challenging as it’s not always easy to control all the variables in a study. This often results in smaller sample sizes , which can reduce the statistical power and generalisability of the results.

Moreover, experimental research design requires random assignment . This means that the researcher needs to assign participants to different groups or conditions in a way that each participant has an equal chance of being assigned to any group (note that this is not the same as random sampling ). Doing so helps reduce the potential for bias and confounding variables . This need for random assignment can lead to ethics-related issues . For example, withholding a potentially beneficial medical treatment from a control group may be considered unethical in certain situations.

Quasi-Experimental Research Design

Quasi-experimental research design is used when the research aims involve identifying causal relations , but one cannot (or doesn’t want to) randomly assign participants to different groups (for practical or ethical reasons). Instead, with a quasi-experimental research design, the researcher relies on existing groups or pre-existing conditions to form groups for comparison.

For example, if you were studying the effects of a new teaching method on student achievement in a particular school district, you may be unable to randomly assign students to either group and instead have to choose classes or schools that already use different teaching methods. This way, you still achieve separate groups, without having to assign participants to specific groups yourself.

Naturally, quasi-experimental research designs have limitations when compared to experimental designs. Given that participant assignment is not random, it’s more difficult to confidently establish causality between variables, and, as a researcher, you have less control over other variables that may impact findings.

All that said, quasi-experimental designs can still be valuable in research contexts where random assignment is not possible and can often be undertaken on a much larger scale than experimental research, thus increasing the statistical power of the results. What’s important is that you, as the researcher, understand the limitations of the design and conduct your quasi-experiment as rigorously as possible, paying careful attention to any potential confounding variables .

Research Design: Qualitative Studies

There are many different research design types when it comes to qualitative studies, but here we’ll narrow our focus to explore the “Big 4”. Specifically, we’ll look at phenomenological design, grounded theory design, ethnographic design, and case study design.

Phenomenological Research Design

Phenomenological design involves exploring the meaning of lived experiences and how they are perceived by individuals. This type of research design seeks to understand people’s perspectives , emotions, and behaviours in specific situations. Here, the aim for researchers is to uncover the essence of human experience without making any assumptions or imposing preconceived ideas on their subjects.

For example, you could adopt a phenomenological design to study why cancer survivors have such varied perceptions of their lives after overcoming their disease. This could be achieved by interviewing survivors and then analysing the data using a qualitative analysis method such as thematic analysis to identify commonalities and differences.

Phenomenological research design typically involves in-depth interviews or open-ended questionnaires to collect rich, detailed data about participants’ subjective experiences. This richness is one of the key strengths of phenomenological research design but, naturally, it also has limitations. These include potential biases in data collection and interpretation and the lack of generalisability of findings to broader populations.

Grounded Theory Research Design

Grounded theory (also referred to as “GT”) aims to develop theories by continuously and iteratively analysing and comparing data collected from a relatively large number of participants in a study. It takes an inductive (bottom-up) approach, with a focus on letting the data “speak for itself”, without being influenced by preexisting theories or the researcher’s preconceptions.

As an example, let’s assume your research aims involved understanding how people cope with chronic pain from a specific medical condition, with a view to developing a theory around this. In this case, grounded theory design would allow you to explore this concept thoroughly without preconceptions about what coping mechanisms might exist. You may find that some patients prefer cognitive-behavioural therapy (CBT) while others prefer to rely on herbal remedies. Based on multiple, iterative rounds of analysis, you could then develop a theory in this regard, derived directly from the data (as opposed to other preexisting theories and models).

Grounded theory typically involves collecting data through interviews or observations and then analysing it to identify patterns and themes that emerge from the data. These emerging ideas are then validated by collecting more data until a saturation point is reached (i.e., no new information can be squeezed from the data). From that base, a theory can then be developed .

As you can see, grounded theory is ideally suited to studies where the research aims involve theory generation , especially in under-researched areas. Keep in mind though that this type of research design can be quite time-intensive , given the need for multiple rounds of data collection and analysis.

Ethnographic Research Design

Ethnographic design involves observing and studying a culture-sharing group of people in their natural setting to gain insight into their behaviours, beliefs, and values. The focus here is on observing participants in their natural environment (as opposed to a controlled environment). This typically involves the researcher spending an extended period of time with the participants in their environment, carefully observing and taking field notes .

All of this is not to say that ethnographic research design relies purely on observation. On the contrary, this design typically also involves in-depth interviews to explore participants’ views, beliefs, etc. However, unobtrusive observation is a core component of the ethnographic approach.

As an example, an ethnographer may study how different communities celebrate traditional festivals or how individuals from different generations interact with technology differently. This may involve a lengthy period of observation, combined with in-depth interviews to further explore specific areas of interest that emerge as a result of the observations that the researcher has made.

As you can probably imagine, ethnographic research design has the ability to provide rich, contextually embedded insights into the socio-cultural dynamics of human behaviour within a natural, uncontrived setting. Naturally, however, it does come with its own set of challenges, including researcher bias (since the researcher can become quite immersed in the group), participant confidentiality and, predictably, ethical complexities . All of these need to be carefully managed if you choose to adopt this type of research design.

Case Study Design

With case study research design, you, as the researcher, investigate a single individual (or a single group of individuals) to gain an in-depth understanding of their experiences, behaviours or outcomes. Unlike other research designs that are aimed at larger sample sizes, case studies offer a deep dive into the specific circumstances surrounding a person, group of people, event or phenomenon, generally within a bounded setting or context .

As an example, a case study design could be used to explore the factors influencing the success of a specific small business. This would involve diving deeply into the organisation to explore and understand what makes it tick – from marketing to HR to finance. In terms of data collection, this could include interviews with staff and management, review of policy documents and financial statements, surveying customers, etc.

While the above example is focused squarely on one organisation, it’s worth noting that case study research designs can have different variation s, including single-case, multiple-case and longitudinal designs. As you can see in the example, a single-case design involves intensely examining a single entity to understand its unique characteristics and complexities. Conversely, in a multiple-case design , multiple cases are compared and contrasted to identify patterns and commonalities. Lastly, in a longitudinal case design , a single case or multiple cases are studied over an extended period of time to understand how factors develop over time.

As you can see, a case study research design is particularly useful where a deep and contextualised understanding of a specific phenomenon or issue is desired. However, this strength is also its weakness. In other words, you can’t generalise the findings from a case study to the broader population. So, keep this in mind if you’re considering going the case study route.

How To Choose A Research Design

Having worked through all of these potential research designs, you’d be forgiven for feeling a little overwhelmed and wondering, “ But how do I decide which research design to use? ”. While we could write an entire post covering that alone, here are a few factors to consider that will help you choose a suitable research design for your study.

Data type: The first determining factor is naturally the type of data you plan to be collecting – i.e., qualitative or quantitative. This may sound obvious, but we have to be clear about this – don’t try to use a quantitative research design on qualitative data (or vice versa)!

Research aim(s) and question(s): As with all methodological decisions, your research aim and research questions will heavily influence your research design. For example, if your research aims involve developing a theory from qualitative data, grounded theory would be a strong option. Similarly, if your research aims involve identifying and measuring relationships between variables, one of the experimental designs would likely be a better option.

Time: It’s essential that you consider any time constraints you have, as this will impact the type of research design you can choose. For example, if you’ve only got a month to complete your project, a lengthy design such as ethnography wouldn’t be a good fit.

Resources: Take into account the resources realistically available to you, as these need to factor into your research design choice. For example, if you require highly specialised lab equipment to execute an experimental design, you need to be sure that you’ll have access to that before you make a decision.

Keep in mind that when it comes to research, it’s important to manage your risks and play as conservatively as possible. If your entire project relies on you achieving a huge sample, having access to niche equipment or holding interviews with very difficult-to-reach participants, you’re creating risks that could kill your project. So, be sure to think through your choices carefully and make sure that you have backup plans for any existential risks. Remember that a relatively simple methodology executed well generally will typically earn better marks than a highly-complex methodology executed poorly.

Recap: Key Takeaways

We’ve covered a lot of ground here. Let’s recap by looking at the key takeaways:

• Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final analysis of data.
• Research designs for quantitative studies include descriptive , correlational , experimental and quasi-experimenta l designs.
• Research designs for qualitative studies include phenomenological , grounded theory , ethnographic and case study designs.
• When choosing a research design, you need to consider a variety of factors, including the type of data you’ll be working with, your research aims and questions, your time and the resources available to you.

If you need a helping hand with your research design (or any other aspect of your research), check out our private coaching services .

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10 Comments

Is there any blog article explaining more on Case study research design? Is there a Case study write-up template? Thank you.

Thanks this was quite valuable to clarify such an important concept.

Thanks for this simplified explanations. it is quite very helpful.

This was really helpful. thanks

Thank you for your explanation. I think case study research design and the use of secondary data in researches needs to be talked about more in your videos and articles because there a lot of case studies research design tailored projects out there.

Please is there any template for a case study research design whose data type is a secondary data on your repository?

This post is very clear, comprehensive and has been very helpful to me. It has cleared the confusion I had in regard to research design and methodology.

This post is helpful, easy to understand, and deconstructs what a research design is. Thanks

how to cite this page

Thank you very much for the post. It is wonderful and has cleared many worries in my mind regarding research designs. I really appreciate .

how can I put this blog as my reference(APA style) in bibliography part?

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How to choose your study design

Affiliation.

• 1 Department of Medicine, Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia.
• PMID: 32479703
• DOI: 10.1111/jpc.14929

Research designs are broadly divided into observational studies (i.e. cross-sectional; case-control and cohort studies) and experimental studies (randomised control trials, RCTs). Each design has a specific role, and each has both advantages and disadvantages. Moreover, while the typical RCT is a parallel group design, there are now many variants to consider. It is important that both researchers and paediatricians are aware of the role of each study design, their respective pros and cons, and the inherent risk of bias with each design. While there are numerous quantitative study designs available to researchers, the final choice is dictated by two key factors. First, by the specific research question. That is, if the question is one of 'prevalence' (disease burden) then the ideal is a cross-sectional study; if it is a question of 'harm' - a case-control study; prognosis - a cohort and therapy - a RCT. Second, by what resources are available to you. This includes budget, time, feasibility re-patient numbers and research expertise. All these factors will severely limit the choice. While paediatricians would like to see more RCTs, these require a huge amount of resources, and in many situations will be unethical (e.g. potentially harmful intervention) or impractical (e.g. rare diseases). This paper gives a brief overview of the common study types, and for those embarking on such studies you will need far more comprehensive, detailed sources of information.

Keywords: experimental studies; observational studies; research method.

© 2020 Paediatrics and Child Health Division (The Royal Australasian College of Physicians).

• Case-Control Studies
• Cross-Sectional Studies
• Research Design*

Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

• When time is an important factor in establishing a relationship between the cause and effect.
• When there is an invariable or never-changing behavior between the cause and effect.
• Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

• There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
• A variable that can be manipulated by the researcher
• Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

• Researchers have firm control over variables to obtain results.
• The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
• The results are specific.
• Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
• Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
• Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

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What is a Research Design? Importance and Types

Why Research Design is Important for a Researcher?

A research design is a systematic procedure or an idea to carry out different tasks of the research study. It is important to know the research design and its types for the researcher to carry out the work in a proper way.

The purpose of research design is that enable the researcher to proceed in the right direction without any deviation from the tasks. It is an overall detailed strategy of the research process.

The design of experiments is a very important aspect of a research study. A poor research design may collapse the entire research project in terms of time, manpower, and money.

7 Importance of Research Design – iLovePhD

What is a Research Design in Research Methodology ?

A research design is a plan or framework for conducting research. It includes a set of plans and procedures that aim to produce reliable and valid data. The research design must be appropriate to the type of research question being asked and the type of data being collected.

A typical research design is a detailed methodology or a roadmap for the successful completion of any research work. ilovephd.com

Importance of Research Design

A Good research design consists of the following important points:

• Formulating a research design helps the researcher to make correct decisions in each and every step of the study.
• It helps to identify the major and minor tasks of the study.
• It makes the research study effective and interesting by providing minute details at each step of the research process.
• Based on the design of experiments (research design), a researcher can easily frame the objectives of the research work.
• A good research design helps the researcher to complete the objectives of the study in a given time and facilitates getting the best solution for the research problems .
• It helps the researcher to complete all the tasks even with limited resources in a better way.
• The main advantage of a good research design is that it provides accuracy, reliability, consistency, and legitimacy to the research.

How to Create a Research Design?                      

According to Thyer, the research design has the following components:

• A researcher begins the study by framing the problem statement of the research work.
• Then, the researcher has to identify the sampling points, the number of samples, the sample size, and the location.
• The next step is to identify the operating variables or parameters of the study and detail how the variables are to be measured.
• The final step is the collection, interpretation, and dissemination of results.

Considerations in selecting the research design

The researchers should know the various types of research designs and their applicability. The selection of a research design can only be made after a careful understanding of the different research design types . The factors to be considered in choosing a research design are

• Qualitative Vs quantitative
• Basic Vs applied
• Empirical Vs Non-empirical

Types of Research Design?

There are four main types of research designs: experimental, observational, quasi-experimental, and descriptive.

• Experimental designs: are used to test cause-and-effect relationships. In an experiment, the researcher manipulates one or more independent variables and observes the effect on a dependent variable.
• Observational designs are used to study behavior without manipulating any variables. The researcher simply observes and records the behavior.
• Quasi-experimental designs are used when it is not possible to manipulate the independent variable. The researcher uses a naturally occurring independent variable and controls for other variables.
• Descriptive designs are used to describe a behavior or phenomenon. The researcher does not manipulate any variables, but simply observes and records the behavior.

I hope, this article would help you to know about what is research design, the types of research design, and what are the important points to be considered in carrying out the research work.

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Modifications of the readiness assessment for pragmatic trials tool for appropriate use with Indigenous populations

• Joanna Hikaka 1 ,
• Ellen M. McCreedy 2 , 3 ,
• Eric Jutkowitz 3 , 4 ,
• Ellen P. McCarthy 5 , 6 &
• Rosa R. Baier 2 , 3  

BMC Medical Research Methodology volume  24 , Article number:  121 ( 2024 ) Cite this article

Metrics details

Inequities in health access and outcomes exist between Indigenous and non-Indigenous populations. Embedded pragmatic randomized, controlled trials (ePCTs) can test the real-world effectiveness of health care interventions. Assessing readiness for ePCT, with tools such as the Readiness Assessment for Pragmatic Trials (RAPT) model, is an important component. Although equity must be explicitly incorporated in the design, testing, and widespread implementation of any health care intervention to achieve equity, RAPT does not explicitly consider equity. This study aimed to identify adaptions necessary for the application of the ‘Readiness Assessment for Pragmatic Trials’ (RAPT) tool in embedded pragmatic randomized, controlled trials (ePCTs) with Indigenous communities.

We surveyed and interviewed participants (researchers with experience in research involving Indigenous communities) over three phases (July-December 2022) in this mixed-methods study to explore the appropriateness and recommended adaptions of current RAPT domains and to identify new domains that would be appropriate to include. We thematically analyzed responses and used an iterative process to modify RAPT.

The 21 participants identified that RAPT needed to be modified to strengthen readiness assessment in Indigenous research. In addition, five new domains were proposed to support Indigenous communities’ power within the research processes: Indigenous Data Sovereignty; Acceptability – Indigenous Communities; Risk of Research; Research Team Experience; Established Partnership). We propose a modified tool, RAPT-Indigenous (RAPT-I) for use in research with Indigenous communities to increase the robustness and cultural appropriateness of readiness assessment for ePCT. In addition to producing a tool for use, it outlines a methodological approach to adopting research tools for use in and with Indigenous communities by drawing on the experience of researchers who are part of, and/or working with, Indigenous communities to undertake interventional research, as well as those with expertise in health equity, implementation science, and public health.

RAPT-I has the potential to provide a useful framework for readiness assessment prior to ePCT in Indigenous communities. RAPT-I also has potential use by bodies charged with critically reviewing proposed pragmatic research including funding and ethics review boards.

Peer Review reports

The World Health Organization defines health equity as ‘the absence of unfair, avoidable or remediable differences among groups of people’ and states ‘health equity is achieved when everyone can attain their full potential for health and wellbeing’ [ 1 ]. Healthcare access and outcomes differ between Indigenous and non-Indigenous populations across the globe, are unfair and unjust, and are therefore defined as health inequities [ 2 ]. These inequities are mediated by colonization and structural racism, which reduce Indigenous peoples’ access to the wider determinants of health, such as education, employment, and healthcare access, further affecting the barriers and enablers of high-quality health care [ 3 ]. To achieve Indigenous health equity [ 4 (p2)] equity must be explicitly incorporated in the design, testing, and widespread implementation of any intervention [ 5 , 6 , 7 , 8 , 9 ]. In practice, this requires researchers to work together with Indigenous communities to understand local contexts and support the achievement of equity by involving Indigenous people as leaders in research, understanding Indigenous priorities, aspirations, and appropriate measures of success [ 5 , 6 , 10 ]. A recent publication of an equity-focused implementation framework provides practical guidance on how to incorporate equity [ 11 ]. The framework is founded on Indigenous rights as set out in New Zealand’s (NZ’s) founding legislative document and includes steps such as defining resources required for equitable implementation [ 11 ].

Centuries of colonial research and inquiry involving subjugation of Indigenous peoples by powerful ‘others’ provides a lineage to contemporary research practices which further exclude and marginalize Indigenous populations [ 7 ]. This exclusion and marginalization is seen in health intervention research. Indigenous populations may be ‘unseen’ through non-reporting of participants’ ethnicity, or under-represented through low Indigenous recruitment [ 12 ]. The design of the trial, outcome measures, or the intervention itself, may be culturally inappropriate or not reflect Indigenous priorities [ 10 , 13 ]. Findings may also be inappropriately framed to focus on individual or cultural deficits rather than service or systematic factors contributing to differences in outcomes between Indigenous and non-Indigenous populations [ 14 , 15 ]. As a result, interventional research that demonstrates benefit in predominately White populations may not be effective, feasible, or acceptable in other cultural settings and research tools developed in non-Indigenous settings have the potential to widen inequities [ 16 ] and lead to unethical research practices in Indigenous populations [ 17 ].

Explicitly designing for equitable health access and outcomes at the outset facilitates pro-equity research. Indigenous pro-equity research may be supported using embedded pragmatic randomized, controlled trials (ePCTs). ePCTs are effectiveness trials that reflect real-world considerations [ 18 ], including ensuring research is appropriate to the targeted communities and settings [ 7 , 19 ]. Further preparatory work is likely required to prepare interventions shown to be effective in predominately non-Indigenous populations for ePCTs in Indigenous populations. Previous Indigenous health intervention research undertaken in Australia, Canada, the United States (US) and NZ has identified processes for investigating how to co-design, implement and evaluate interventions in Indigenous settings [ 20 ], adapting interventions prior to ePCT [ 21 ], how to ensure low resource environments are ready to implement an intervention within a ePCT [ 22 ], as well as targeting specific research processes, such as recruitment [ 23 ].

An intervention must be sufficiently ‘ready’ for an ePCT to ensure it will be feasible to conduct and possible to draw appropriate conclusions from the findings [ 24 ]. The Readiness Assessment for Pragmatic Trials (RAPT) model is an implementation science tool to help researchers qualitatively assess an intervention’s ‘readiness’ (low to high) in the context the intervention’s current state and likelihood of intervention adoption if proven effective in ePCT [ 25 ]. There are nine domains with accompanying questions and scoring criteria: [ 25 ]

Implementation protocol

Is there an implementation protocol that is sufficiently detailed to enable replication?

What is the extent of evidence to support intervention efficacy?

Is the safety of the intervention known?

Feasibility

To what extent can the intervention be implemented within the current environment?

Measurement

To what extent can the intervention effectiveness be measured, ideally using pragmatic outcome measures?

Is the intervention likely to be economically viable?

Acceptability

How likely is it that providers will adopt the intervention?

To what extent is the intervention in alignment with stakeholders’ priorities?

How likely is it that the results for the ePCT will inform clinical practice and/or policy?

RAPT’s readiness domains were defined based on discussion amongst experts at a US National Institute on Aging workshop. However, the resulting model does not explicitly include health equity [ 26 ] and has not been applied to pro-equity Indigenous health intervention research. If adapted to include Indigenous equity considerations, RAPT may inform such efforts. This study aimed to identify adaptions necessary for RAPT’s application to ePCTs with Indigenous communities.

Study design

This mixed-methods study used an online questionnaire and semi-structured interviews. This study was approved by the Auckland Health Research Ethics Committee (AH24242).

This research was led by JH, an Indigenous health services researcher from NZ with experience in Indigenous research methodology and qualitative research, including inductive thematic analysis in Indigenous research underpinned by Indigenous theory. She was working at a university in the US at the time this research was undertaken and worked in collaboration with the rest of the research team who are the lead authors of the RAPT model. Our research team had expertise in qualitative research, co-design and co-creation, public health, health equity, survey methodology, quality improvement, and clinical care in older adult settings. The researchers recognise the right of Indigenous peoples, and the right of people living with dementia, to experience equitable health outcomes.

Recruitment and consent

Eligibility.

Participants were eligible if they were 18 years or older and had been involved as a researcher (self-identified, no formal qualifications required) in research relating to non-pharmacological dementia care interventions in Indigenous communities in NZ (Māori) or the United States (US; American Indian, Native Alaskan, and Kānaka Maoli/Native Hawaiian peoples). We focused on dementia interventions because RAPT, although since applied more broadly [ 27 ], was initially developed to assess dementia interventions [ 25 ] and because this work was partially conducted in partnership with the US National Institute on Aging (NIA) IMPACT Collaboratory, which focuses on dementia interventions. NZ and the US were the countries of interest as the lead author is an Indigenous researcher from NZ and was a visiting scholar, collaborating with the US authors of RAPT.

Recruitment

We first conducted a literature search to identify peer-reviewed publications relating to non-pharmacological dementia interventions (any study design) that included Indigenous populations in the US or NZ and were published from 2011 to 2022. We then emailed invitations to all identified authors for whom we could obtain email addresses ( n  = 77). We also emailed invitations to directors of three Indigenous ageing research centers and the International Indigenous Dementia Research Network. We used snowball techniques to identify additional potential study participants [ 28 ]. Participants provided informed consent using an online form immediately prior to completing the online questionnaire.

Questionnaire development and data collection

We surveyed participants in July and August 2022. We provided brief introductory material regarding RAPT. We then asked participants to complete the questionnaire ( Supplementary material ). We collected all data using Qualtrics® (Seattle, Washington US).

Demographics and research experience

The questionnaire captured respondents’ demographics, including self-identified ethnicity and research experience.

RAPT domain questionnaire

We asked participants first to reflect on their research experiences, then to rate each RAPT domain’s appropriateness for interventional research with Indigenous communities using a 4-point Likert scale (inappropriate, slightly inappropriate, slightly appropriate, appropriate). We also asked participants to indicate whether ‘to adequately incorporate health equity’ a domain needed any modifications or should be removed. If they advised modifications, we asked for specific suggestions.

Semi-structured interviews and consensus building

After modifying the existing RAPT domains and adding new domains based on participants’ questionnaire responses, we drafted a modified RAPT, termed the RAPT-Indigenous (RAPT-I). We conducted a semi-structured in-depth interview with respondents to the online questionnaire component (‘respondents’) who assented to participate in follow-up interviews (November-December 2022). Questionnaire respondents were invited to participate rather than new participants to continue development and refinement of domains, similar to the approach taken in a Delphi consensus approach [ 29 ] and to methods used in other similar implementation science research [ 22 ]. The lead author (JH) conducted all interviews using Zoom™ (San Jose, California US) and transcribed the interviews. We provided participants (interviewees) with the draft RAPT-I via email at the time of scheduling the interview, encouraging them to review draft RAPT-I ahead of the interview. During interviews, we explored interviewees perspectives about RAPT-I; any guidance that should accompany the tool; whether ePCTs in Indigenous populations should proceed with low readiness in various domains; and the modified tool’s utility with marginalized populations other than Indigenous communities. Interviewees could request a recording of their interview within two weeks of the interview. An iterative process was used to make further modifications to the draft RAPT-I based on interview responses.The lead researcher sent a second RAPT-I draft to all interviewees and invited them to review and suggest additional modifications prior to RAPT-I’s finalisation. Interviewees provided further feedback either in written form or via a video conference where notes were taken by the lead researcher.

Data analysis

We used Microsoft Excel® (Seattle, Washington US) to characterize participants using descriptive statistics. We calculated the percentage of participants who selected each Likert response when asked about each domain’s appropriateness and need for modification. The lead researcher used the current domains as a framework to group qualitative feedback from the questionnaire and interviews that related to each of the existing domains [ 30 ] and used general inductive analysis to generate new domains from free-text questionnaire responses and interview transcripts to develop new domains (Fig.  1 ). This preliminary analysis was presented to all other authors for discussion and review, with raw data being supplied as required during discussions. A general inductive approach was chosen as this method aligns with our intent to condense and summarize extensive and varied raw data and to develop a model [ 31 ], in this case a modification of RAPT. We included quotes from respondents (‘R’) in the results. We did not undertake any subgroup analysis. For each of the stages that involved iterative changes to draft versions of RAPT-I, the lead researcher made initial changes which were then discussed with all other authors for consensus building and finalization of draft versions. The lead author undertook the final iterative review process which produced a third draft that was finalized, through consultation and discussion with the full research team, for presentation in this paper.

Participant flow through study

Sample size

We targeted 30 participants to reach saturation of responses to qualitative questionnaire questions. We aimed for approximately 15 participants from each country and at least 10 who self-identified as Indigenous.

We emailed questionnaire invitations to 77 people and 21 (27·3%) responded. Research experience ranged from 5 to 40 years (median 20 years); experience focused on older adult/dementia research, 3–40 years (median: 10 years); and with Indigenous research 4–18 years (median: 6). Two-thirds of participants were from NZ ( n  = 14, 66·7%). About half identified as Indigenous ( n  = 10, 47·6%) or White ( n  = 9, 42·9%); the remainder, non-Indigenous ethnic/racial minorities ( n  = 2, 9·5%).

Seven (33·3%) questionnaire respondents participated in follow-up interviews, which lasted 26–30 min (median: 28 min). Research experience ranged from 5 to 28 years (median: 14 years). Four interviewees (57·1%) were from NZ; all but one ( n  = 6, 85·7%) identified as Indigenous; one an ethnic minority; and three were female (42·9%). Following the interviews, three interviewees (42·9%) reviewed the draft RAPT-I. Saturation of ideas in response to qualitative survey questions was achieved. Saturation of interviewee responses was not sought although saturation was largely achieved after the fifth interview. Interviewees suggested further changes to the first draft RAPT-I which focused on the clarification of domain and scoring wording to convey intended meaning, highlighting the importance of Indigenous partnership, and the value of accompanying guidance to support the use of RAPT-I.

All nine domains were assessed as being appropriate or slightly appropriate by most participants (Table  1 ); however, most participants (90·5%) indicated that some modifications were needed to increase appropriateness for use with Indigenous populations. A greater proportion of respondents would use a modified version of RAPT ( n  = 15, 71·4%) vs. the original ( n  = 8, 38·1%).

General summary of questionnaire responses to existing domains

Although respondents felt many domains were general enough to be appropriate, most recommended including explicit guidance regarding the intent to achieve Indigenous health equity and to minimise potential risks associated with the intervention or research process. Many felt such guidance would promote culturally-safe interventions and research practices, help researchers to identify areas to strengthen before an ePCT, and even provide a framework for critical review by funders and ethics boards.

The goal of [using a tool such as RAPT] is that health equity becomes part and parcel of how we do high quality research. (R10, US, non-Indigenous ethnic minority)

At the same time, they expressed any guidance provided needed to support meaningful assessment rather than performative assessment that did not change approaches to research.

The question is, will it become another tick box exercise? (R3, NZ, Indigenous)

All respondents felt that it was appropriate to have an implementation protocol that considered equity through all aspects of implementation.

The parameters and criterion of health equity should be demonstrated. (R15, NZ, Indigenous)

However, many noted there were likely to be aspects of pragmatic research with Indigenous communities that could not be protocolised. Others noted that even if a protocol enabled replicability, replicating an intervention tested in one Indigenous community in another Indigenous community may be inappropriate.

There is a need to recognize the flexibility necessary for Indigenous research, I believe there is an option between partially documented and fully documented, for flexible documentation that is mostly (or partially) documented, that is revised during the research journey. (R4, NZ, Indigenous)

Many respondents deemed Evidence essential; however, most recommended requiring evidence with the targeted Indigenous community specifically. Several questioned the need for efficacy evidence from randomized-controlled trials, which may not be available for Indigenous communities.

The ideal is to have prior evidence, however there may not be prior evidence for Indigenous populations. Sometimes a number of less rigorous methods is good enough evidence for the intervention to be tested. (R4, NZ, Indigenous)

Some respondents also felt that it was important to modify Evidence to include evidence of access- and equity-related outcomes. One noted that one purpose for conducting research with Indigenous communities may be that interventions efficacious in other populations either do not achieve equity or worsen inequities in Indigenous populations.

In many instances of health equity research, there may not be any existing efficacy studies. I mean, a large part of the drivers of inequity are that interventions are NOT fit for purpose and it is precisely because of this that new interventions are being proposed and researched!” (R3, NZ, Indigenous).

Most participants agreed that Feasibility was important and that understanding feasibility specifically in the targeted Indigenous community was crucial, as it may differ across populations. For example, some participants shared that it may not be possible to adequately implement a new intervention with existing resources in already under-resourced communities or populations.

[Feasibility] tends to be neglected as work is moved into communities. (R9, US, non-Indigenous)

Several participants felt that additional support (e.g., human and financial resourcing) may be required to investigate a new intervention and that such needs should not be reason to withhold research opportunities from communities already experiencing inequitable resourcing.

Many participants felt that pragmatic outcome data collection could be beneficial, but potentially unachievable for Indigenous interventions, for example if structural inequities impacted the availability and use of electronic health records systems.

Rural indigenous communities do not have outcomes “routinely captured” due to lack of health care / poor health care services. (R7, US, Indigenous)

One participant questioned the ability of electronic systems to accurately capture measures, noting that ethnic minority populations are routinely misclassified and undercounted in NZ national data sets. Another suggested that measurement readiness could be expanded to include two items: one focused on exploring electronic data collection; the other, on using easily collected and entered hand-written data collection.

Most participants deemed the economic viability important for sustainability and evidence-based resource allocation. However, they felt that expertise in cost-benefit analysis in the Indigenous communities of interest would be important to appropriately account for economic costs or benefits particular to the community of interest and to consider the wider influences and impacts of inequitable resourcing in health service/system infrastructure and in the social determinants of health.

To achieve equity the costs are often higher in these populations to achieve the same level of intervention/outcome. (R6, NZ, non-Indigenous)

Equally, some participants described the need to take a broad approach to assessing benefits through an Indigenous lens, e.g., improvement in spiritual wellbeing or social connectedness.

Importantly, some participants related Cost to Evidence, noting lack of evidence in Indigenous communities would affect cost-benefit analysis calculations or considerations. Several felt that lack of cost data or low readiness should not prevent investigation of potentially beneficial interventions in “understudied, underserved, and minoritized groups”.

Some participants were unclear about the distinctions among Acceptability, Alignment, and Impact and suggested adding wording to clarify differences. As currently framed, Acceptability and Alignment domains focus on the existing relevance to internal and external stakeholders, whereas Impact domain focuses on the potential value of future ePCT findings [ 12 ]. Participants felt Impact aligned with Indigenous values by appropriately focusing on the potential for translation into practice, but that the domain needed to focus on benefit for Indigenous communities and inform or relate to equitable clinical care and policy.

Impact should be Indigenous focused. A focus group would be better able to define how this would look when considering what qualifies as meaningful “impact”.” (R7, US, Indigenous).

Further respondent quotes are shown in Table  2 .

New domains

General thematic analysis of questionnaire and interview responses led to the development of five new domains: Indigenous Data Sovereignty; Acceptability – Indigenous communities; Risk of research; Research team experience; Established partnership.

Acceptability – Indigenous communities

Several participants recognized the importance of Acceptability to ensure the intervention reflects providers’ priorities and is implemented as intended specifically in Indigenous communities. However, they raised the need to engage providers in preparatory work relating to health equity to ensure or increase acceptance and therefore the potential for intervention success, especially if intervention elements or implementation approaches differed from practices used by staff from dominant cultures in implementation sites.

Health equity research findings can be confronting to many in the mainstream who don’t believe there is a problem. (R3, NZ, Indigenous)

Most participants suggested broadening Acceptability to include the community in which the intervention will be examined, as without this acceptance the intervention is also likely to fail.

[We need to think about] how we make research attractive to Indigenous communities” (R7, US, Indigenous).

Participants felt Alignment was critical to Indigenous intervention development and implementation, like Acceptance. Many felt that the requirement for Indigenous stakeholders’ values and priorities needed to be explicitly stated. Participants mentioned the potential for stakeholders to hold competing priorities and some stated that Indigenous priorities need to be privileged above other stakeholders’. Although several participants recognised the need for some alignment between all stakeholders, including Indigenous stakeholders, they questioned what course of action to take when health systems or providers disagreed or did not value equity as a priority.

Important question, but how are community needs balanced with stakeholder needs? (R19, NZ, Indigenous)

Risk of research

Most respondents felt that understanding potential risks in Indigenous communities was essential for assessing readiness. In fact, some felt that researchers should assess risk first and not assess other domains or proceed with an ePCT if risk was unknown or there was potential for harm. Importantly, they described considering risk from the perspectives of both participants and the wider community, and not just risks associated with the intervention, but with the research process as a whole.

What is deemed as a risk? What might not be a risk for non-Indigenous peoples might be a risk for Indigenous peoples. Is the intervention culturally appropriate? Could possibly consider the benefits for Indigenous peoples too. Thinking about collective risk of intervention not just individual risk . (R20, NZ, Indigenous)

Research team experience

Several respondents felt that lack of evidence in Indigenous populations could be overcome by adapting interventions proven efficacious in other populations in partnership with Indigenous communities, without the need for further testing ahead of ePCTs. Some felt Evidence should consider the Indigenous practices in place and valued for decades or centuries, and not be limited to Western approaches to evidence. To undertake this however, it was identified that at least some members of the research team should have experience working with Indigenous communities to support these practices and that Indigenous researchers and communities should be part of the research team.

[Indigenous communities] have to be part of the research team from the start, deciding the questions, methods and protocols. (R19, NZ, Indigenous)

Respondents commented that such guidance accompanying RAPT-I would be particularly important for research teams with limited health equity experience; they felt that such researchers often want to do the right thing but lack the expertise to plan for equity.

Established partnership

Respondents discussed the importance of collaboration with Indigenous communities to assess each domain and facilitate culturally-appropriate modifications. They felt that the type and extent of preparatory work required prior to moving forward with an ePCT should be done by researchers and Indigenous communities together and that a modified RAPT could provide a useful framework for such planning and work. For example, some suggested modifying the domain to ensure the protocol be developed in partnership with Indigenous communities, explicitly consider health equity, and be written in culturally-appropriate and accessible language. Many emphasized the importance of engaging the Indigenous community to assess feasibility and recommended providing guidance to help researchers and communities identify all aspects of feasibility that should be assessed. Several participants also suggested identifying the communities’ opportunities and strengths which facilitated feasible implementation, rather than only shortcomings. Others noted that established partnerships would support the inclusion of outcome measures of most importance or relevance to Indigenous communities and that pre-work should ensure that planned outcomes are appropriate.

Importantly, respondents discussed the need to establish partnerships between researchers and Indigenous communities very early in the process.

Partnership discussions should be part of the initial engagement. (R7, US, Indigenous)

Indigenous Data Sovereignty

Interview participants highlighted the importance of Indigenous Data Sovereignty, with one respondent saying it was so important that it should be prioritized as the first domain. Respondents stated that Indigenous Data Sovereignty needed to be considered and discussed right from the outset and that these discussions were likely to be fundamental to partnership establishment and intervention implementation. Respondents felt that decisions that upheld Indigenous Data Sovereignty needed to be ongoing throughout the research process and therefore, that a shared understanding of the need for ongoing discussion was needed prior to e-PCT. Respondents also advised the framing of Indigenous Data Sovereignty guidance and scoring was important to demonstrate that research processes needed to ensure Indigenous rights to data sovereignty could be exercised.

Indigenous communities will always have sovereignty over their data, but they may not have the infrastructure in place to exercise the sovereignty over their data. (R5, US, Indigenous)

Questionnaires and interviews with researchers conducting non-pharmacological dementia care interventions with Indigenous communities in NZ and the US resulted in recommendations to modify RAPT to explicitly incorporate considerations for pro-equity research in Indigenous communities. Recommendations for RAPT-I included new guidance for existing RAPT domains and the addition of new domains focused on Indigenous rights to culturally-safe research practices and to govern and control research processes. Participants also discussed how RAPT-I could guide researchers with limited experience with equity-focused research and emphasized the importance of assessing and modifying interventions in collaboration with Indigenous communities.

Others have previously raised the need for implementation science theory and methods to adequately incorporate health equity [ 32 , 33 , 34 ]. Without doing so, traditional implementation science will likely widen disparities, moving us further away from the goal of health equity [ 33 ]. Similar to our study findings, exploring and addressing power dynamics, working in partnership with the goal of developing sustainable models of care, and examination of wider structural systems that impact on interventions and their impacts have been deemed important [ 32 ]. As in our study, methods that facilitate and provide guidance on how to effectively design for equity when implementing an intervention have been identified as crucial [ 33 , 34 ]. An example of how this is done in practice is provided by the National Institute of Aging IMPACT Collaboratory, which has produced guidance documents on ‘Best Practices for Integrating Health Equity into Embedded Pragmatic Clinical Trials for Dementia Care’ to step researchers through equity considerations at all stages of ePCT from community engagement and study design through implementation and analysis [ 35 ]. This type of tool, along with implementation frameworks addressing equity in Indigenous populations [ 11 ], could be used alongside RAPT-I, providing guidance on next steps if RAPT-I identifies low readiness in one or more of the domains.

Previous Canadian research investigating practices that support cultural safety in controlled trials with Indigenous peoples identified that effective communication and co-design between researchers and Indigenous communities and critical reflection in response to cultural ‘mistakes’ fostered success in research [ 36 ]. Indigenous peoples’ rights to control and power within research appeared to be recognised by participants who sought mechanisms within RAPT-I to protect and uphold these rights in implementation research. This included understanding the participation, and potential risk to communities, as a collective rather than solely as individuals, as well as recognising strengths and opportunities within communities. The CONSIDER statement [ 6 ] was developed to facilitate full and complete reporting of research that involves Indigenous peoples, however it also provides a framework through which to plan research that upholds Indigenous rights. Application of the CONSIDER statement would also be useful for planning for ePCT readiness in Indigenous communities.

Some of the concepts that are incorporated within RAPT-I domains have been previously described in pragmatic controlled trial literature with Indigenous populations. These include developing effective relationships which give power to Indigenous communities [ 36 ], Indigenous community endorsement of ePCT prior to initiation [ 37 ], relationships, assessing community and researcher readiness to commence the ePCT [ 22 ]. Previous work has identified ten principles of practice when undertaking health research with Indigenous Australians, although not specifically related to ePCT [ 38 ]. The adaption of interventions for ePCT with Indigenous communities has also been described, with methods for adaption including community involvement and focussing on strengths within Indigenous communities [ 39 ] and inclusion of culturally relevant values and materials [ 40 , 41 ]. A recent scoping review identified that although participatory research approaches with Indigenous communities are needed for appropriate adaption, this is done with varying authenticity and success, and authors noted that clearer guidance was needed to facilitate improved practices [ 42 ]. Our research further builds on these works and brings together considerations relating to both intervention implementation and research processes in one tool for researchers and communities to access and guide them through an explicit ePCT readiness assessment process.

It is widely acknowledged that pragmatism of a clinical trial should be viewed on a continuum [ 18 ] and participants felt RAPT-I could provide a useful framework for researchers and Indigenous communities to critically and collaboratively evaluate readiness in Indigenous and equity focused contexts. Where there was low or medium readiness in some domains, participants felt that this would not necessarily prevent progression to ePCT, but that researchers and Indigenous communities should have collaborative discussions with decisions made about the preparatory work which could increase readiness. This may include small pilot or feasibility studies to better understand some aspects of the intervention and research processes.

Where research was not feasible due to structural factors such as chronic under resourcing as seen in other studies [ 43 , 44 ], thought should be given to whether these could be corrected in the short-term. For example, those delivering the intervention could be resourced through research funding during the research contract, alongside researchers working with other stakeholders to advocate for and develop stable resourcing for sustainable service models in the long-term. This highlights the potential of researchers as advocates for structural change within health research resourcing. This includes a responsibility to monitor RAPT-I utilization to ensure it is used to strengthen research undertaken in and with Indigenous communities rather than impeding Indigenous progress.

RAPT was designed to help researchers make informed decisions about whether a particular intervention is ready to undergo real-world effectiveness testing and to identify areas that may need to be addressed prior to an ePCT. RAPT-I has the potential to also provide a useful framework for those charged with critically reviewing proposed pragmatic research, including funding and ethics review boards. Further study is warranted to pilot and refine RAPT-I within a broader context including non-dementia focused research and in Indigenous settings outside of NZ and the US. Further investigation to provide RAPT-I assessment exemplars, evaluate language accessibility, assess applicability in these additional settings and to explore how RAPT-I could be the basis for a broader health equity extension which would have applicability in the vast majority of ePCT readiness assessments would be beneficial.

Strengths and limitations

A strength of this study was that the research team, and participants, had collective expertise in Indigenous health research, health equity, intervention science methodology and ePCT study design. Fewer participants than anticipated were required to reach data saturation in the online questionnaire. We only included researchers from the UA and NZ it is likely that this work can be progressed further by including other Indigenous populations and researchers. Participants were recruited from dementia-related studies only and widening the inclusion criteria may have led to more diverse discussion. Findings therefore may not be able to generalized for other study settings. Although participants with experience with research including Indigenous populations was sought, Indigenous health services research and development, or health equity more generally, may not have been their area of expertise.

This study highlights the specific strategies to incorporate Indigenous health equity considerations into RAPT and offers RAPT-I as a proposed modified assessment. New domains have been proposed which advocate for the rights of Indigenous communities to be partners in research and maintain sovereignty over research data. RAPT-I provides a potential mechanism to increase the robustness of readiness assessment for ePCT by researchers and Indigenous communities.

Data availability

Data is not available as use by third parties was not granted in the ethics process.

Abbreviations

Embedded pragmatic randomized, controlled trials

New Zealand

Readiness Assessment for Pragmatic Trials

Readiness Assessment for Pragmatic Trials – Indigenous

United States of America

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Acknowledgements

The authors thank the participants who generously contributed their time and expertise including the following who chose to be acknowledged by name: Lauren W. Yowelunh McLester-Davis.

This research was supported in part by the Health Research Council of New Zealand (HRC: 21/1062). Drs. Baier, Jutkowitz, McCreedy and McCarthy were supported by the National Institute on Aging (NIA) of the National Institutes of Health under Award Number U54AG063546, which funds NIA Imbedded Pragmatic Alzheimer’s Disease and AD-Related Dementias Clinical Trials Collaboratory (NIA IMPACT Collaboratory). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funders had no role or influence over the study design, the collection, analysis and interpretation, or reporting of the data.

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JH and RB conceptualized the study and wrote the main manuscript. All authors (JH, EMM, EJ, EPM, RB) contributed to study design, contributed to data analysis and reviewed the manuscript.

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Hikaka, J., McCreedy, E.M., Jutkowitz, E. et al. Modifications of the readiness assessment for pragmatic trials tool for appropriate use with Indigenous populations. BMC Med Res Methodol 24 , 121 (2024). https://doi.org/10.1186/s12874-024-02244-z

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Preparedness for a first clinical placement in nursing: a descriptive qualitative study

• Philippa H. M. Marriott 1 ,
• Jennifer M. Weller-Newton 2   nAff3 &
• Katharine J. Reid 4  

BMC Nursing volume  23 , Article number:  345 ( 2024 ) Cite this article

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A first clinical placement for nursing students is a challenging period involving translation of theoretical knowledge and development of an identity within the healthcare setting; it is often a time of emotional vulnerability. It can be a pivotal moment for ambivalent nursing students to decide whether to continue their professional training. To date, student expectations prior to their first clinical placement have been explored in advance of the experience or gathered following the placement experience. However, there is a significant gap in understanding how nursing students’ perspectives about their first clinical placement might change or remain consistent following their placement experiences. Thus, the study aimed to explore first-year nursing students’ emotional responses towards and perceptions of their preparedness for their first clinical placement and to examine whether initial perceptions remain consistent or change during the placement experience.

The research utilised a pre-post qualitative descriptive design. Six focus groups were undertaken before the first clinical placement (with up to four participants in each group) and follow-up individual interviews ( n  = 10) were undertaken towards the end of the first clinical placement with first-year entry-to-practice postgraduate nursing students. Data were analysed thematically.

Three main themes emerged: (1) adjusting and managing a raft of feelings, encapsulating participants’ feelings about learning in a new environment and progressing from academia to clinical practice; (2) sinking or swimming, comprising students’ expectations before their first clinical placement and how these perceptions are altered through their clinical placement experience; and (3) navigating placement, describing relationships between healthcare staff, patients, and peers.

Conclusions

This unique study of first-year postgraduate entry-to-practice nursing students’ perspectives of their first clinical placement adds to the extant knowledge. By examining student experience prior to and during their first clinical placement experience, it is possible to explore the consistency and change in students’ narratives over the course of an impactful experience. Researching the narratives of nursing students embarking on their first clinical placement provides tertiary education institutions with insights into preparing students for this critical experience.

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First clinical placements enable nursing students to develop their professional identity through initial socialisation, and where successful, first clinical placement experiences can motivate nursing students to persist with their studies [ 1 , 2 , 3 , 4 ]. Where the transition from the tertiary environment to learning in the healthcare workplace is turbulent, it may impact nursing students’ learning, their confidence and potentially increase attrition rates from educational programs [ 2 , 5 , 6 ]. Attrition from preregistration nursing courses is a global concern, with the COVID-19 pandemic further straining the nursing workforce; thus, the supply of nursing professionals is unlikely to meet demand [ 7 ]. The COVID-19 pandemic has also impacted nursing education, with student nurses augmenting the diminishing nursing workforce [ 7 , 8 ].

The first clinical placement often triggers immense anxiety and fear for nursing students [ 9 , 10 ]. Research suggests that among nursing students, anxiety arises from perceived knowledge deficiencies, role ambiguity, the working environment, caring for ‘real’ people, potentially causing harm, exposure to nudity and death, and ‘not fitting in’ [ 2 , 3 , 11 ]. These stressors are reported internationally and often relate to inadequate preparation for entering the clinical environment [ 2 , 10 , 12 ]. Previous research suggests that high anxiety before the first clinical placement can be related to factors likely to affect patient outcomes, such as self-confidence and efficacy [ 13 ]. High anxiety during clinical placement may impair students’ capacity to learn, thus compromising the value of the clinical environment for learning [ 10 ].

The first clinical placement often occurs soon after commencing nursing training and can challenge students’ beliefs, philosophies, and preconceived ideas about nursing. An experience of cultural or ‘reality’ shock often arises when entering the healthcare setting, creating dissonance between reality and expectations [ 6 , 14 ]. These experiences may be exacerbated by tertiary education providers teaching of ‘ideal’ clinical practice [ 2 , 6 ]. The perceived distance between theoretical knowledge and what is expected in a healthcare placement, as opposed to what occurs on clinical placement, has been well documented as the theory-practice gap or an experience of cognitive dissonance [ 2 , 3 ].

Given the pivotal role of the first clinical placement in nursing students’ trajectories to nursing practice, it is important to understand students’ experiences and to explore how the placement experience shapes initial perceptions. Existing research focusses almost entirely either on describing nursing students’ projected emotions and perceptions prior to undertaking a first clinical placement [ 3 ] or examines student perceptions of reflecting on a completed first placement [ 15 ]. We wished to examine consistency and change in student perception of their first clinical placement by tracking their experiences longitudinally. We focused on a first clinical placement undertaken in a Master of Nursing Science. This two-year postgraduate qualification provides entry-to-practice nursing training for students who have completed any undergraduate qualification. The first clinical placement component of the course aimed to orient students to the clinical environment, support students to acquire skills and develop their clinical reasoning through experiential learning with experienced nursing mentors.

This paper makes a significant contribution to understanding how nursing students’ perceptions might develop over time because of their clinical placement experiences. Our research addresses a further gap in the existing literature, by focusing on students completing an accelerated postgraduate two-year entry-to-practice degree open to students with any prior undergraduate degree. Thus, the current research aimed to understand nursing students’ emotional responses and expectations and their perceptions of preparedness before attending their first clinical placement and to contrast these initial perceptions with their end-of-placement perspectives.

Study design

A descriptive qualitative study was undertaken, utilising a pre- and post-design for data collection. Focus groups with first-year postgraduate entry-to-practice nursing students were conducted before the first clinical placement, with individual semi-structured interviews undertaken during the first clinical placement.

Setting and participants

All first-year students enrolled in the two-year Master of Nursing Science program ( n  = 190) at a tertiary institution in Melbourne, Australia, were eligible to participate. There were no exclusion criteria. At the time of this study, students were enrolled in a semester-long subject focused on nursing assessment and care. They studied the theoretical underpinnings of nursing and science, theoretical and practical nursing clinical skills and Indigenous health over the first six weeks of the course. Students completed a preclinical assessment as a hurdle before commencing a three-week clinical placement in a hospital setting, a subacute or acute environment. Overall, the clinical placement aimed to provide opportunities for experiential learning, skill acquisition, development of clinical reasoning skills and professional socialisation [ 16 , 17 ].

In total, sixteen students participated voluntarily in a focus group of between 60 and 90 min duration; ten of these students also participated in individual interviews of between 30 and 60 min duration, a number sufficient to reach data saturation. Table  1 shows the questions used in the focus groups conducted before clinical placement commenced and the questions for the semi-structured interview questions conducted during clinical placement. Study participants’ undergraduate qualifications included bachelor’s degrees in science, arts and business. A small number of participants had previous healthcare experience (e.g. as healthcare assistants). The participants attended clinical placement in the Melbourne metropolitan, Victorian regional and rural hospital locations.

Data collection

The study comprised two phases. The first phase comprised six focus groups prior to the first clinical placement, and the second phase comprised ten individual semi-structured interviews towards the end of the first clinical placement. Focus groups (with a maximum of four participants) and individual interviews were conducted by the lead author online via Zoom and were audio-recorded. Capping group size to a relatively small number considered diversity of perceptions and opportunities for participants to share their insights and to confirm or contradict their peers, particularly in the online environment [ 18 , 19 ].

Focus groups and interview questions were developed with reference to relevant literature, piloted with volunteer final-year nursing students, and then verified with the coauthors. All focus groups and interviewees received the same structured questions (Table  1 ) to ensure consistency and to facilitate comparison across the placement experience in the development of themes. Selective probing of interviewees’ responses for clarification to gain in-depth responses was undertaken. Nonverbal cues, impressions, or observations were noted.

The lead author was a registered nurse who had a clinical teaching role within the nursing department and was responsible for coordinating clinical placement experiences. To ensure rigour during the data collection process, the lead author maintained a reflective account, exploring her experiences of the discussions, reflecting on her interactions with participants as a researcher and as a clinical educator, and identifying areas for improvement (for instance allowing participants to tell their stories with fewer prompts). These reflections in conjunction with regular discussion with the other authors throughout the data collection period, aided in identifying any researcher biases, feelings and thoughts that possibly influenced the research [ 20 ].

To maintain rigour during the data analysis phase, we adhered to a systematic process involving input from all authors to code the data and to identify, refine and describe the themes and subthemes reported in this work. This comprehensive analytic process, reported in detail in the following section, was designed to ensure that the findings arising from this research were derived from a rigorous approach to analysing the data.

Data analysis

Focus groups and interviews were transcribed using the online transcription service Otter ( https://otter.ai/ ) and then checked and anonymised by the first author. Preliminary data analysis was carried out simultaneously by the first author using thematic content analysis proposed by Braun and Clarke [ 21 ] using NVivo 12 software [ 22 ]. All three authors undertook a detailed reading of the first three transcripts from both the focus groups and interviews and independently identified major themes. This preliminary coding was used as the basis of a discussion session to identify common themes between authors, to clarify sources of disagreement and to establish guidelines for further coding. Subsequent coding of the complete data set by the lead author identified a total of 533 descriptive codes; no descriptive code was duplicated across the themes. Initially, the descriptive codes were grouped into major themes identified from the literature, but with further analysis, themes emerged that were unique to the current study.

The research team met frequently during data analysis to discuss the initial descriptive codes, to confirm the major themes and subthemes, to revise themes on which there was disagreement and to identify any additional themes. Samples of quotes were reviewed by the second and third authors to decide whether these quotes were representative of the identified themes. The process occurred iteratively to refine the thematic categories, to discuss the definitions of each theme and to identify exemplar quotes.

Ethical considerations

The lead author was a clinical teacher and the clinical placement coordinator in the nursing department at the time of the study. Potential risks of perceived coercion and power imbalances were identified because of the lead author’s dual roles as an academic and as a researcher. To manage these potential risks, an academic staff member who was not part of the research study informed students about the study during a face-to-face lecture and ensured that all participants received a plain language statement identifying the lead author’s role and how perceived conflicts of interest would be managed. These included the lead author not undertaking any teaching or assessment role for the duration of the study and ensuring that placement allocations were completed prior to undertaking recruitment for the study. All students who participated in the study provided informed written consent. No financial or other incentives were offered. Approval to conduct the study was granted by the University of Melbourne Human Research Ethics Committee (Ethics ID 1955997.1).

Three main themes emerged describing students’ feelings and perceptions of their first clinical placement. In presenting the findings, before or during has been assigned to participants’ quotes to clarify the timing of students’ perspectives related to the clinical placement.

Major theme 1: Adjusting and managing a raft of feelings

The first theme encompassed the many positive and negative feelings about work-integrated learning expressed by participants before and during their clinical placement. Positive feelings before clinical placement were expressed by participants who were comfortable with the unknown and cautiously optimistic.

I am ready to just go with the flow, roll with the punches (Participant [P]1 before).

Overwhelmingly, however, the majority of feelings and thoughts anticipating the first clinical placement were negatively oriented. Students who expressed feelings of fear, anxiety, lack of knowledge, lack of preparedness, uncertainty about nursing as a career, or strong concerns about being a burden were all classified as conveying negative feelings. These negative feelings were categorised into four subthemes.

Subtheme 1.1 I don’t have enough knowledge

All participants expressed some concerns and anxiety before their first clinical placement. These encompassed concerns about knowledge inadequacy and were linked to a perception of under preparedness. Participants’ fears related to harming patients, responsibility for managing ‘real’ people, medication administration, and incomplete understanding of the language and communication skills within a healthcare setting. Anxiety for many participants merged with the logistics and management of their life during the clinical placement.

I’m scared that they will assume that I have more knowledge than I do (P3 before). I feel quite similar with P10, especially when she said fear of unknown and fear that she might do something wrong (P9 before).

Subtheme 1.2 Worry about judgment, being seen through that lens

Participants voiced concerns that they would be judged negatively by patients or healthcare staff because they perceived that the student nurse belonged to specific social groups related to their cultural background, ethnicity or gender. Affiliation with these groups contributed to students’ sense of self or identity, with students often describing such groups as a community. Before the clinical placement, participants worried that such judgements would impact the support they received on placement and their ability to deliver patient care.

Some older patients might prefer to have nurses from their own background, their own ethnicity, how they would react to me, or if racism is involved (P10 before). I just don’t want to reinforce like, whatever negative perceptions people might have of that community (P16 before).

Participants’ concerns prior to the first clinical placement about judgement or poor treatment because of patients’ preconceived ideas about specific ethnic groups did not eventuate.

I mean, it didn’t really feel like very much of a thing once I was actually there. It is one of those things you stress about, and it does not really amount to anything (P16 during).

Some students’ placement experiences revealed the positive benefits of their cultural background to enhancing patient care. One student affirmed that the placement experience reinforced their commitment to nursing and that this was related to their ability to communicate with patients whose first language was not English.

Yeah, definitely. Like, I can speak a few dialects. You know, I can actually see a difference with a lot of the non-English speaking background people. As soon as you, as soon as they’re aware that you’re trying and you’re trying to speak your language, they, they just open up. Yeah, yes. And it improves the care (P10 during).

However, a perceived lack of judgement was sometimes attributed to wearing the full personal protective equipment required during the COVID-19 pandemic, which meant that their personal features were largely obscured. For this reason, it was more difficult for patients to make assumptions or attributions about students’ ethnic or gender identity based on their appearance.

People tend to assume and call us all girls, which was irritating. It was mostly just because all of us were so covered up, no one could see anyone’s faces (P16 during).

Subtheme 1.3 Is nursing really for me?

Prior to their first clinical placement experience, many participants expressed ambivalence about a nursing career and anticipated that undertaking clinical placement could determine their suitability for the profession. Once exposed to clinical placement, the majority of students were completely committed to their chosen profession, with a minority remaining ambivalent or, in rare cases, choosing to leave the course. Not yet achieving full commitment to a nursing career was related to not wishing to work in the ward they had for their clinical placement, while remaining open to trying different specialities.

I didn’t have an actual idea of what I wanted to do after arts, this wasn’t something that I was aiming towards specifically (P14 before). I think I’m still not 100%, but enough to go on, that I’m happy to continue the course as best as I can (P11 during).

Subtheme 1.4 Being a burden

Before clinical placement, participants had concerns about being burdensome and how this would affect their clinical placement experiences.

If we end up being a burden to them, an extra responsibility for them on top of their day, then we might not be treated as well (P10 before).

A sense of burden remained a theme during the clinical placement for participants for the first five to seven days, after which most participants acknowledged that their role became more active. As students contributed more productively to their placement, their feelings of being a burden reduced.

Major theme 2: Sinking or swimming

The second major theme, sinking or swimming, described participants’ expectations about a successful placement experience and identified themes related to students’ successes (‘swimming’) or difficulties (‘sinking’) during their placement experience. Prior to clinical placement, without a realistic preview of what the experience might entail, participants were uncertain of their role, hoped for ‘nice’ supervising nurses and anticipated an observational role that would keep them afloat.

I will focus on what I want to learn and see if that coincides with what is expected, I guess (P15 before).

During the clinical placement, the reality was very different, with a sense of sinking. Participants discovered, some with shock, that they were expected to participate actively in the healthcare team.

I got the sense that they were not going to muck around, and, you know, they’re ‘gonna’ use the free labour that came with me (P1 during).

Adding to the confusion about the expected placement experience, participants believed that healthcare staff were unclear about students’ scope of practice for a postgraduate entry-to-practice degree, creating misalignment between students’ and supervising nurses’ expectations.

It seems to me like the educators don’t really seem to have a clear picture of what the scope is, and what is actually required or expected of us (P10 during).

In exploring perceived expectations of the clinical placement and the modifying effect of placement on initial expectations, three subthemes were identified: translation to practice is overwhelming, trying to find the rhythm or jigsaw pieces, and individual agency.

Subtheme 2.1 Translation to practice is overwhelming

Before clinical placement, participants described concerns about insufficient knowledge to enable them to engage effectively with the placement experience.

If I am doing an assessment understanding what are those indications and why I would be doing it or not doing it at a certain time (P1 before).

Integrating and applying theoretical content while navigating an unfamiliar clinical environment created a significant gap between theory and practice during clinical placement. As the clinical placement experience proceeded and initial fears dissipated, students became more aware of applying their theoretical knowledge in the clinical context.

We’re learning all this theory and clinical stuff, but then we don’t really have a realistic idea of what it’s like until we’re kind of thrown into it for three weeks (P10 during).

Subtheme 2.2 Trying to find the rhythm or the jigsaw pieces

Before clinical placement, participants described learning theory and clinical skills with contextual unfamiliarity. They had the jigsaw pieces but did not know how to assemble it; they had the music but did not know the final song. When discussing their expectations about clinical placement, the small number of participants with a healthcare background (e.g. as healthcare assistants) proposed realistic answers, whereas others struggled to answer or cited stories from friends or television. With a lack of context, feelings of unpreparedness were exacerbated. Once in the clinical environment, participants further emphasised that they could not identify what they needed to know to successfully prepare for clinical placement.

It was never really pieced together. We’ve learned bits and pieces, and then we’re putting it together ourselves (P8 during). On this course I feel it was this is how you do it, but I did not know how it was supposed to be played, I did not know the rhythm (P4 during).

Subtheme 2.3 Individual agency

Participants’ individual agency, their attitude, self-efficacy, and self-motivation affected their clinical placement experiences. Participant perceptions in advance of the clinical placement experience remained consistent with their perspectives following clinical placement. Before clinical placement, participants who were highly motivated to learn exhibited a growth mindset [ 23 ] and planned to be proactive in delivering patient care. During their clinical placement, initially positive students remained positive and optimistic about their future. Participants who believed that their first clinical placement role would be largely observational and were less proactive about applying their knowledge and skills identified boredom and a lack of learning opportunities on clinical placement.

A shadowing position, we don’t have enough skills and authority to do any work, not do any worthwhile skills (P3 before). I thought it would be a lot busier, because we’re limited with our scope, so there’s not much we can do, it’s just a bit slower than I thought (P12 during).

Individual agency appears to influence a successful first clinical placement; other factors may also be implicated but were not the focus of this study. Further research exploring the relationships between students’ age, life experience, resilience, individual agency, and the use of coping strategies during a first clinical placement would be useful.

Major theme 3: The reality of navigating placement relationships

The third main theme emphasised the reality of navigating clinical placement relationships and explored students’ relationships with healthcare staff, patients, and peers. Before clinical placement, many participants, especially those with healthcare backgrounds, expressed fears about relationships with supervising nurses. They perceived that the dynamics of the team and the healthcare workplace might influence the support they received. Several participants were nervous about attending placement on their own without peers for support, especially if the experience was challenging. Participants identified expectations of being mistreated, believing that it was unavoidable, and prepared themselves to not take it personally.

For me it’s where we’re going to land, are we going to be in a supportive, kind of nurturing environment, or is it just kind of sink or swim? (P5 before). If you don’t really trust them, you’re nervous the entire time and you’ll be like what if I get it wrong (P16 before).

Despite these concerns, students strongly emphasised the value of relationships during their first clinical placement, with these perceptions unchanged by their clinical placement experience. Where relationships were positive, participants felt empowered to be autonomous, and their self-confidence increased.

You get that that instant reaction from the patients. And that makes you feel more confident. So that really got me through the first week (P14 during). I felt like I was intruding, then as I started to build a bit of rapport with the people, and they saw that I was around, I don’t feel that as much now (P1 during).

Such development hinged on the receptiveness and support of supervising nurses, the team on the ward, and patients and could be hindered by poor relationships.

He was the old-style buddy nurse in his fifties, every time I questioned him, he would go ssshh, just listen, no questions, it was very stressful (P10 during). It depends whether the buddy sees us as an extra pair of hands, or we’re learners (P11 during).

Where students experienced poor behaviour from supervising nurses, they described a range of emotional responses to these interactions and also coping strategies including avoiding unfriendly staff and actively seeking out those who were more inclusive.

If they weren’t very nice, it wouldn’t be very enjoyable and if they didn’t trust you, then it would be a bit frustrating, that like I can do this, but you won’t let me (P12 during). If another nurse was not nice to me, and I was their buddy, I would literally just not buddy with them and go and follow whoever was nice to me (P4 during).

Relationships with peers were equally important; students on clinical placement with peers valued the shared experience. In contrast, students who attended clinical placement alone at a regional or rural hospital felt disconnected from the opportunities that learning with peers afforded.

Our research explored the emotional responses and perceptions of preparedness of postgraduate entry-to-practice nursing students prior to and during their first clinical placement. In this study, we described how the perceptions of nursing students remained consistent or were modified by their clinical placement experiences. Our analysis of students’ experiences identified three major themes: adjusting and managing a raft of feelings; sinking or swimming; and the reality of navigating placement relationships. We captured similar themes identified in the literature; however, our study also identified novel aspects of nursing students’ experiences of their first clinical placement.

The key theme, adjusting and managing a raft of feelings, which encapsulates anxiety before clinical placement, is consistent with previous research. This theme included concerns in communicating with healthcare staff and managing registered nurses’ negative attitudes and expectations, in addition to an academic workload [ 11 , 24 ]. Concerns not previously identified in the literature included a fear of judgement or discrimination by healthcare staff or patients that might impact the reputation of marginalised communities. Fortunately, these initial fears largely dissipated during clinical placement. Some students discovered that a diverse cultural background was an asset during their clinical placement. Although these initial fears were ameliorated by clinical placement experiences, evidence of such fears before clinical placement is concerning. Further research to identify appropriate support for nursing students from culturally diverse or marginalised communities is warranted. For example, a Finnish study highlighted the importance of mentoring culturally diverse students, creating a pedagogical atmosphere during clinical placement and integrating cultural diversity into nursing education [ 25 ].

Preclinical expectations of being mistreated can be viewed as an unavoidable phenomenon for nursing students [ 26 ]. The existing literature highlights power imbalances and hierarchical differences within the healthcare system, where student nurses may be marginalised, disrespected, and ignored [ 9 , 27 , 28 ]. During their clinical placement, students in our study reported unintentional incivility by supervising nurses: feeling not wanted, ignored, or asked to remain quiet by supervising nurses who were unfriendly or highly critical. These findings were similar to those of Thomas et al.’s [ 29 ] UK study and were particularly heightened at the beginning of clinical placement. Several students acknowledged that nursing staff fatigue from a high turnover of students on their ward and the COVID-19 pandemic could be contributing factors. In response to such incivility, students reported decreased self-confidence and described becoming quiet and withdrawing from active participation with their patients. Students oriented their behaviour towards repetitive low-level tasks, aiming to please and help their supervising nurse, to the detriment of learning opportunities. Fortunately, these incidents did not appear to impact nursing students’ overall experience of clinical placement. Indeed, students found positive experiences with different supervising nurses and their own self-reflection assisted with coping. Other active strategies to combat incivility identified in the current study that were also identified by Thomas et al. [ 29 ] included avoiding nurses who were uncivil, asking to work with nurses who were ‘nice’ to them, and seeking out support from other staff as a coping strategy. The nursing students in our study were undertaking a postgraduate entry-to-practice qualification and already had an undergraduate degree. The likely greater levels of experience and maturity of this cohort may influence their resilience when working with unsupportive supervising nurses and identifying strategies to manage challenging situations.

The theory-practice gap emerged in the theme of sinking or swimming. A theory-practice gap describes the perceived dissonance between theoretical knowledge and expectations for the first clinical placement, as opposed to the reality of the experience, and has been reported in previous studies (see, for instance, 24 , 30 , 31 , 32 ). Existing research has shown that when the first clinical placement does not meet inexperienced student nurses’ expectations, a disconnect between theory and practice occurs, creating feelings of being lost and insecure within the new environment, potentially impacting students’ motivation and risk of attrition [ 19 , 33 ]. The current study identified further areas exacerbating the theory-practice gap. Before the clinical placement, students without a healthcare background lacked context for their learning. They lacked understanding of nurses’ shift work and were apprehensive about applying clinical skills learned in the classroom. Hence, some students were uncertain if they were prepared for their first clinical placement or even how to prepare, which increased their anxiety. Prior research has demonstrated that applying theoretical knowledge more seamlessly during clinical placement was supported when students knew what to expect [ 6 ]. For instance, a Canadian study exposed students as observers to the healthcare setting before starting clinical placement, enabling early theory to practice connections that minimised misconceptions and false assumptions during clinical placement [ 34 ].

In the current study, the theory-practice gap was further exacerbated during clinical placement, where healthcare staff were confused about students’ scope of practice and the course learning objectives and expectations in a postgraduate entry-to-practice nursing qualification. The central booking system for clinical placements classifies first-year nursing students who participated in this study as equivalent to second-year undergraduate nursing students. Such a classification could create a misalignment between clinical educators’ expectations and their delivery of education versus students’ actual learning needs and capacity [ 3 , 31 ]. Additional communication to healthcare partners is warranted to enhance understanding of the scope of practice and expectations of a first-year postgraduate entry-to-practice nursing student. Educating and empowering students to communicate their learning needs within their scope of practice is also required.

Our research identified a link between students’ personality traits or individual agency and their first clinical placement experience. The importance of a positive orientation towards learning and the nursing profession in preparedness for clinical placement has been highlighted in previous studies [ 31 ]. Students’ experience of their first clinical placement in our study appeared to be strongly influenced by their mindset [ 23 ]. Some students demonstrated motivation to learn, were happy to ‘roll with the punches’, yet remain active in their learning requirements, whereas others perceived their role as observational and expected supervising nurses to provide learning opportunities. Students who anticipated a passive learning approach prior to their first clinical placement reported boredom, limited activity, and lack of opportunities during their first clinical placement. These students could have a lowered sense of self-efficacy, which may lead to a greater risk of doubt, stress, and reduced commitment to the profession [ 35 ]. Self-efficacy theory explores self-perceived confidence and competence around people’s beliefs in their ability to influence events, which is associated with motivation and is key to nursing students progressing in their career path confidently [ 35 , 36 ]. In the current study, students who actively engaged in their learning process used strategies such as self-reflection and sought support from clinical educators, peers and family. Such active approaches to learning appeared to increase their resilience and motivation to learn as they progressed in their first clinical placement.

Important relationships with supervising nurses, peers, or patients were highlighted in the theme of the reality of navigating placement relationships. This theme links with previous research findings about belongingness. Belongingness is a fundamental human need and impacts students’ behaviour, emotions, cognitive processes, overall well-being, and socialisation into the profession [ 37 , 38 ]. Nursing students who experience belongingness feel part of a team and are more likely to report positive experiences. Several students in the current study described how feeling part of a team improved self-confidence and empowered work-integrated learning. Nonetheless, compared with previous literature (see for instance, 2), working as a team and belongingness were infrequent themes. Such infrequency could be related to the short duration of the clinical placement. In shorter clinical placements, nursing students learn a range of technical skills but have less time to develop teamwork skills and experience socialisation to the profession [ 29 , 39 ].

Positive relationships with supervising nurses appeared fundamental to students’ experiences. Previous research has shown that in wards with safe psycho-social climates, where the culture tolerates mistakes, regarding them as learning opportunities, a pedagogical atmosphere prevails [ 25 , 39 ]. Whereas, if nursing students experience insolent behaviours or incivility, this not only impacts learning it can also affect career progression [ 26 ]. Participants who felt safe asking questions were given responsibility, had autonomy to conduct skills within their scope of practice and thrived in their learning. This finding aligns with previous research affirming that a welcoming and supportive clinical placement environment, where staff are caring, approachable and helpful, enables student nurses to flourish [ 36 , 40 , 41 , 42 ]. Related research highlights that students’ perception of a good clinical placement is linked to participation within the community and instructor behaviour over the quality of the clinical environment and opportunities [ 27 , 28 ]. Over a decade ago, a large European study found that the single most important element for students’ clinical learning was the supervisory relationship [ 39 ]. In our study, students identified how supervising nurses impacted their emotions and this was critical to their experience of clinical placement, rather than how effective they were in their teaching, delivery of feedback, or their knowledge base.

Students’ relationships with patients were similarly important for a successful clinical placement. Before the clinical placement, students expressed anxiety and fears in communicating and interacting with patients, particularly if they were dying or acutely unwell, which is reflective of the literature [ 2 , 10 , 11 ]. However, during clinical placement, relationships with patients positively impacted nursing students’ experiences, especially at the beginning when they felt particularly vulnerable in a new environment. Towards the end of clinical placement, feelings of incompetence, nervousness and uncertainty had subsided. Students were more active in patient care, which increased self-confidence, empowerment, and independence, in turn further improving relationships with patients and creating a positive feedback loop [ 36 , 42 , 43 ].

Limitations

This study involved participants from one university and a single course, thus limiting the generalisability of the results. Thus, verification of the major themes identified in this research in future studies is needed. Nonetheless, the purpose of this study was to explore in detail the way in which the experiences of clinical placement for student nurses modified initial emotional responses towards undertaking placement and their perceptions of preparedness. Participants in this study undertook their clinical placement in a variety of different hospital wards in different specialties, which contributed to the rigour of the study in identifying similar themes in nursing students’ experiences across diverse placement contexts.

This study explored the narratives of first-year nursing students undertaking a postgraduate entry-to-practice qualification on their preparedness for clinical placement. Exploring students’ changing perspectives before and during the clinical placement adds to extant knowledge about nursing students’ emotional responses and perceptions of preparedness. Our research highlighted the role that preplacement emotions and expectations may have in shaping nursing students’ clinical placement experiences. Emerging themes from this study highlighted the importance students placed on relationships with peers, patients, and supervising nurses. Significant anxiety and other negative emotions experienced by nursing students prior to the first clinical placement suggests that further research is needed to explore the impact of contextual learning to scaffold students’ transition to the clinical environment. The findings of this research also have significant implications for educational practice. Additional educational support for nursing students prior to entering the clinical environment for the first time might include developing students’ understanding of the clinical environment, such as through increasing students’ understanding of the different roles of nurses in the clinical context through pre-recorded interviews with nurses. Modified approaches to simulated teaching prior to the first clinical placement would also be useful to increase the emphasis on students applying their learning in a team-based, student-led context, rather than emphasising discrete clinical skill competencies. Finally, increasing contact between students and university-based educators throughout the placement would provide further opportunities for students to debrief, to receive support and to manage some of the negative emotions identified in this study. Further supporting the transition to the first clinical placement could be fundamental to reducing the theory-practice gap and allaying anxiety. Such support is crucial during their first clinical placement to reduce attrition and boost the nursing workforce.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to the conditions of our ethics approval but may be available from the corresponding author on reasonable request and subject to permission from the Human Research Ethics Committee.

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Acknowledgements

The authors wish to thank the first-year nursing students who participated in this study and generously shared their experiences of undertaking their first clinical placement.

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Jennifer M. Weller-Newton

Present address: School of Nursing and Midwifery, University of Canberra, Kirinari Drive, Bruce, Canberra, ACT, 2617, Australia

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Department of Nursing, The University of Melbourne, Grattan St, Parkville, VIC, 3010, Australia

Philippa H. M. Marriott

Department of Rural Health, The University of Melbourne, Grattan St, Shepparton, VIC, 3630, Australia

Present address: Department of Medical Education, Melbourne Medical School, The University of Melbourne, Grattan St, Parkville, VIC, 3010, Australia

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All authors made a substantial contribution to conducting the research and preparing the manuscript for publication. P.M., J.W-N. and K.R. conceptualised the research and designed the study. P.M. undertook the data collection, and all authors were involved in thematic analysis and interpretation. P.M. wrote the first draft of the manuscript, K.R. undertook a further revision and all authors contributed to subsequent versions. All authors approved the final version for submission. Each author is prepared to take public responsibility for the research.

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Marriott, P.H.M., Weller-Newton, J.M. & Reid, K.J. Preparedness for a first clinical placement in nursing: a descriptive qualitative study. BMC Nurs 23 , 345 (2024). https://doi.org/10.1186/s12912-024-01916-x

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A Review on Hybrid Fiber-Reinforced Self-compacting Concrete: Properties & Challenges

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The material known as Hybrid Fiber Reinforced Self-Compacting Concrete (HF SCC) combines the advantages of SCC with several fiber kinds, including glass, steel, and synthetic fibers. This article provides an overview of HF SCC-related research and findings, focusing on new and challenging features, practical applications, sustainability, and design and technology developed for the HF SCC standards. The HF SCC study investigated the impact of fiber, fiber fraction, and composite design on performance, strength, toughness, and durability. Clinical studies have shown that adding fiber to SCC improves its mechanical properties including tensile strength, fracture toughness, etc. The interactions between the fibers and other components of the concrete matrix were analyzed to understand the mechanisms behind the development of HF SCC. Sustainability factors and environmental considerations are important in the development and usage of products. We conducted a life cycle valuation to assess the environmental influence of HF SCC and explore the usage of recycled materials and sustainable practices. The outcomes highlight the potential of HF SCCs to contribute to sustainable development by reducing carbon emissions, reducing waste and improving resource efficiency. Key outcomes from the above discourse include the recognition of Hybrid Fiber Self-Compacting Concrete (HFSCC) as a promising solution in construction, owing to its ability to enhance mechanical properties through the incorporation of various fiber types. Extensive research has deepened our understanding of HFSCC’s behavior and operation, highlighting its efficacy across diverse applications. However, further exploration is warranted to address research gaps, particularly in understanding penetration, shrinkage, and resistance to environmental factors. Promoting HFSCC adoption, advancing sustainable practices, and standardizing design and manufacturing processes are crucial steps towards realizing its full potential in the construction industry.

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Dahake, H.B., Shinde, B.H. A Review on Hybrid Fiber-Reinforced Self-compacting Concrete: Properties & Challenges. Iran J Sci Technol Trans Civ Eng (2024). https://doi.org/10.1007/s40996-024-01480-z

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Study designs

Shraddha parab.

Department of Clinical Pharmacology, Seth GS Medical College and KEM Hospital, Parel, Mumbai - 400 012, India

Supriya Bhalerao

1 Department of Clinical Pharmacology, TNMC and BYL Nair Hospital, Mumbai Central, Mumbai - 400 008, India

In the last issue, we discussed "sample size", one of the crucial aspects when planning a clinical study. This article discusses another statistically important issue, Study designs. Study design is a process wherein the trial methodology and statistical analysis are organized to ensure that the null hypothesis is either accepted or rejected and the conclusions arrived at reflect the truth.

The design of any study is more important than analyzing its results, as a poorly designed study can never be recovered, whereas a poorly analyzed study can be reanalyzed to reach a meaningful conclusion.[ 1 ] Rather, the design of the study decides how the data generated can be best analyzed. The scientific integrity of the study and the credibility of the data from the study thus substantially depend on the study design.

The various aspects of clinical research can be broadly divided into two types, viz., observational and experimental. The basic difference between these two types is that the earlier does not involve any intervention (drug treatment/therapeutic procedures/diagnostic tools), whereas in an experimental study, the investigator administers an intervention to patients and the effect of this intervention on the course of events is documented. Let us see the different designs which are commonly used to conduct these two types of researches.

D ESCRIPTIVE S TUDY

This is the first foray into research. These studies describe the frequency, natural history and determinants of a factor/disease. It is a study to identify patterns or trends in a situation, but not the cause and effect (causal) linkages among its different elements, e.g. a study to assess the predominant prakriti in hypertensive patients only helps in determining the predominant prakriti in these patients, it does not establish a linkage that a specific prakriti is a causative factor for hypertension. Types of descriptive studies are prevalence surveys, case series, surveillance data and analysis of routinely collected data, etc.

Case series and case reports

A case report is a descriptive study of a single individual, whereas case series is a study of a small group. In these studies, the possibility of an association between an observed effect and a specific environmental exposure is studied based on detailed clinical evaluations and histories of the individual(s).

They are most likely to be useful when the disease is uncommon and caused exclusively by a single kind of exposure (e.g. vinyl chloride and angiosarcoma or diethylstilbestrol (DES) and clear-cell carcinoma of the vagina).[ 2 ] Case reports (or case series) may be first to provide clues in identifying a new disease or adverse health effect from an exposure.

A NALYTICAL S TUDY

These studies are generally (although not always) used to test one or more specific hypotheses, typically whether an exposure is a risk factor for a disease or an intervention is effective in preventing or curing disease (or any other occurrence or condition of interest). Of course, data obtained in an analytic study can also be explored in a descriptive mode, and data obtained in a descriptive study can be analyzed to test hypotheses, making it analytical. In short, these studies are designed to examine etiology and causal associations. Types of analytical studies are cross sectional, case-control, cohort (retrospective and prospective) and ecological.

Cross sectional

Cross-sectional study is also known as a prevalence study. It measures the cause and effect at the same time, but does not tell us the relationship, i.e. which one is the cause and which one is the effect. This is the commonest study design used in general practice and research, in general. These studies are relatively easy to do, inexpensive and can be carried out in a short time frame.

Case-control

In studies using this particular design, patients who already have a certain condition (cases) are compared (e.g. diabetic patients with hospitalization) with people who do not have that condition (controls) (e.g. diabetic patients without hospitalization). The researcher goes through the past records of these subjects (both cases and controls) to find out whether the development of the condition only in one group of patients is due to presence of some causative factor (exposure). Thus, in a typical case-control study, the data collection is mainly retrospective (backward in time) [ Figure 1 ].

Case-Control design

These studies are less reliable than either randomized controlled trials or cohort studies. A major drawback to case-control studies is that one cannot measure the risk of developing a particular outcome because of an exposure. Additionally, in these studies one has to mainly rely on the memory of patients to identify what in the past might have caused their current disease, which is most often of long latency. This might induce a bias while analyzing the results, which is known as "recall bias". Because human memory is frequently imprecise, recall bias is commonly believed to be "pervasive in case-control studies."[ 3 ]

The presence of disease affects both the patient's perception of the causes and his search for possible exposure to a hypothesized risk factor. Therefore, the recall of remote exposures in case-control studies is commonly presumed to be differential among study subjects depending on their disease status.[ 4 ] Data, even about irrelevant exposures, are often remembered better by cases or/and underreported by controls.[ 5 ] This trend in exposure recall tends to inflate the risk estimate in case-control studies. Also, recalling the exact timing of exposure, which is often important in determining temporality of an association and in estimating induction period of a disease, can be differential among exposed cases and exposed controls.

Despite the fact that recall bias is a major limitation of case-control studies, a number of methodological strategies documented in the literature can minimize the recall bias.[ 6 ]

The advantages of case-control studies are that they can be done quickly and are very efficient for conditions/diseases with rare outcomes.[ 7 ]

Cohort (Longitudinal studies)

A cohort study begins with a group of subjects with some causative factor (e.g. daily intake of Virrudha ahar ) but free of the condition of interest (e.g. skin diseases). All the subjects are followed up and observed for the occurrence of the condition of interest.

In contrast to the case-control study, a cohort study is usually prospective (forward in time). It provides the best information about the cause of disease plus the most direct measurement of the risk of developing a particular outcome due to exposure [ Figure 2 ].

Cohort (Longitudinal studies) design

These studies, however, require a large number of subjects and a long period of follow up to assess whether the event of interest has occurred, due to which these studies are very expensive to conduct. The main drawback of these studies due to long follow up is that there are high chances of subjects getting lost to follow up.[ 7 ] If in the two groups, the degree of such losses is substantially different, it can lead to bias and false positive results.

Correlational studies

These studies (sometimes called ecologic studies) explore the statistical connection between disease in different population groups and estimated exposures in groups rather than individuals. For example, they may correlate death rates by country with estimates of exposure, such as factory emissions in a given geographic area, proximity to waste sites, or air or water pollution levels. The geographical information system (GIS) is a very useful new tool that improves the ability of ecologic studies to be able to determine a link between health data and a source of environmental exposure.

C ONTROLLED S TUDIES

These studies have control groups (i.e. comparator that can either be a standard drug or placebo). Controlled trials can be clinical trials (unit of randomization is an individual) or community trials (unit of randomization is a community or cluster).

Nonrandomized controlled

This is an experimental study in which people are allocated to different interventions using methods that are not random. In these studies, allocation to different groups is done arbitrarily. This kind of study design may sometimes overestimate the advantages of one treatment over other.

Randomized controlled

Randomized controlled trials (RCTs) are considered the "gold standard" in medical research since they offer the best answers about the effectiveness of different therapies or interventions.

The important aspect of this study design is that the patients are randomly assigned to the study all groups that help in avoiding bias in patient allocation-to-treatment that a physician might be subject to [ Figure 3 ]. It also increases the probability that the differences between the groups can be attributed only to the treatment(s) under study.

Randomised clinical trial

There are certain types of questions where randomized controlled studies cannot be done for ethical reasons, for instance, if patients are asked to undertake harmful experiences (like smoking) or denied any treatment beyond a placebo when there are known effective treatments.

There are different types of randomized studies as follows.[ 8 ]

In parallel studies, treatment and controls are allocated to different individuals. This is unlike a crossover study where at first one group receives treatment A, followed by treatment B later, while the other group receives treatment B followed by treatment A [ Figure 4 ].

Parallel design

In case of Ayurvedic studies, an extension of this design known as "add-on" design is useful, where one group receives standard treatment, while the other group receives standard treatment along with Ayurvedic treatment. Using these studies, comparison of relative or absolute efficacy can be obtained in a short period. However, these studies generally require large number of patients for the analysis.

In these types of studies each patient serves as his own control. Each patient gets both drugs; the order in which the patient gets each drug is randomized [ Figure 5 ]. Generally, it requires a smaller sample size.[ 9 ]

Cross over design

Assumptions

The effects of intervention during the first period do not carry over into the second period.

Internal factors (e.g. disease severity) and external factors (e.g. season), which can affect the efficacy of the drug/s, are constant over time.

Ideally, the patient's disease condition should return to its baseline state after discontinuation of the first treatment.

In case of Ayurvedic studies, this design can prove useful as each patient serves as his own control and this way the individualistic approach of Ayurveda gets conserved even in clinical studies. However, at the same time it is difficult to implement as the "wash out period" (duration between two treatments to wash out the effect of the first so that it does not get carry over) cannot be defined in view of unavailability of pharmacokinetic data of Ayurvedic treatments.

Studies involving two or more factors while randomizing are called factorial designs [ Figure 6 ]. A factor is simply a categorical variable (e.g. age and prakriti ) with two or more values, referred to as levels.

Factorial design

Factorial design permits researchers to investigate the joint effect of two or more factors on a dependent variable (e.g. weight). The factorial design also facilitates the study of interactions, illuminating the effects of different conditions of the experiment on identifiable subgroups of subjects participating in the experiment.

It is a type of randomized controlled trial wherein groups of participants (as opposed to individual participants) are randomized. Cluster randomized controlled trials are also known as cluster randomized trials, group randomized trials, and place randomized trials.

Advantages of cluster randomized controlled trials over individually randomized controlled trials include the ability to study interventions that cannot be directed toward selected individuals (e.g. a radio show about lifestyle changes) and the ability to control for "contamination" across individuals (e.g. one individual's change in behavior may influence another individual to do so too). Disadvantages compared with individually randomized controlled trials include greater complexity in design and analysis and a requirement for more participants to obtain the same statistical power.

Quasi-randomized

In these studies, participant allocation is done using schemes such as date of birth (odd or even), number of the hospital record, date at which they are invited to participate in the study (odd or even), or alternatively into different study groups.

A quasi-randomized trial uses quasi-random method of allocating participants to different interventions. There is a greater risk of selection bias in quasi-random trials where allocation is not adequately concealed compared with randomized controlled trials with adequate allocation concealment.

Acknowledgments

The authors sincerely acknowledge Dr. Jaideep Gogtay, Medical Director, Cipla Ltd., for his technical help.

Source of Support: Nill

Conflict of Interest: None declared

R EFERENCES