data analysis in nursing research

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Nursing Research Using Data Analysis

Qualitative designs and methods in nursing.

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Mary De Chesnay, PhD, RN, PMHCNS-BC, FAAN

This is a concise, step-by-step guide to conducting qualitative nursing research using various forms of data analysis. It is part of a unique series of books devoted to seven different qualitative designs and methods in nursing, written for both novice researchers and specialists seeking to develop or expand their competency. This practical resource encompasses such methodologies as content analysis, a means of organizing and interpreting data to elicit themes and concepts; discourse analysis, used to analyze language to understand social or historical context; narrative analysis, in which the researcher seeks to understand human experience through participant stories; and focus groups and case studies, used to understand the consensus of a group or the experience of an individual and his or her reaction to a difficult situation such as disease or trauma.

Written by a noted qualitative research scholar and contributing experts, the book describes the philosophical basis for conducting research using data analysis and delivers an in-depth plan for applying its methodologies to a particular study, including appropriate methods, ethical considerations, and potential challenges. It presents practical strategies for solving problems related to the conduct of research using the various forms of data analysis and presents a rich array of case examples from published nursing research. These include author analyses to support readers in decision making regarding their own projects. The book embraces such varied topics as data security in qualitative research, the image of nursing in science fiction literature, the trajectory of research in several nursing studies throughout Africa, and many others. Focused on the needs of both novice researchers and specialists, it will be of value to health institution research divisions, in-service educators and students, and graduate nursing educators and students.

Key Features:

• Explains how to conduct nursing research using content analysis, discourse analysis, narrative analysis, and focus groups and case studies
• Presents state-of-the-art designs and protocols
• Focuses on solving practical problems related to the conduct of research
• Features rich nursing exemplars in a variety of health/mental health clinical settings in the United States and internationally

Contributors

Foreword Linda Roussel, PhD, RN

Series Foreword

Acknowledgments

1 Qualitative Data Analysis

Jennifer B. Averill

2 Data Security in Qualitative Research

Grady D. Barnhill and Elizabeth A. Barnhill

3 Stories of Caring for Others by Nursing Students in Cameroon, Africa

Mary Bi Suh Atanga and Sarah Hall Gueldner

4 The Image of Nursing in the Science-Fiction Literature

Linda Wright Thompson

5 African Indigenous Methodology in Qualitative Research: The Lekgotla —A Holistic Approach of Data Collection and Analysis Intertwined

Abel Jacobus Pienaar

6 Understanding Talk and Texts: Discourse Analysis for Nursing Research

Jennifer Smith-Merry

7 Exploring Discourse in Context: Discussion of the Use of Foucauldian Discourse Analysis and Critical Discourse Analysis to Compare Managerial and Organizational Discourses

Susan L. Johnson

8 Narrative Analysis: A Qualitative Method for Positive Social Change

Michelle M. McKelvey

9 Learning From Others: Writing a Qualitative Dissertation

Judith Hold

10 Key Informant Interviews and Focus Groups

Gloria Ann Jones Taylor and Barbara Jean Blake

11 Using Focus Group Discussion to Investigate Perceptions of Sexual Risk Compensation Following Posttrial HIV Vaccine Uptake Among Young South Africans

Catherine MacPhail

12 Data Analysis: The World Café

Magdalena P. Koen, Emmerentia du Plessis, and Vicki Koen

Appendix A List of Journals That Publish Qualitative Research

Mary de Chesnay

Appendix B Essential Elements for a Qualitative Proposal

Tommie Nelms

Appendix C Writing Qualitative Research Proposals

Joan L. Bottorff

Appendix D Outline for a Research Proposal

Mary de Chesnay, PhD, RN, PMHCNS-BC, FAAN , is professor at Kennesaw State University, School of Nursing, Kennesaw, Georgia.

• Release Date: December 5, 2014
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• eBook ISBN: 9780826126894

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Measurement in Nursing Research

Curtis, Alexa Colgrove PhD, MPH, FNP, PMHNP; Keeler, Courtney PhD

Alexa Colgrove Curtis is assistant dean and professor of graduate nursing and director of the MPH–DNP dual degree program and Courtney Keeler is an associate professor, both at the University of San Francisco School of Nursing and Health Professions. Contact author: Alexa Colgrove Curtis, [email protected] . Nursing Research, Step by Step is coordinated by Bernadette Capili, PhD, NP-C: [email protected] . The authors have disclosed no potential conflicts of interest, financial or otherwise. A podcast with the authors is available at www.ajnonline.com .

Editor's note: This is the fourth article in a series on clinical research by nurses. The series is designed to give nurses the knowledge and skills they need to participate in research, step by step. Each column will present the concepts that underpin evidence-based practice—from research design to data interpretation. The articles will be accompanied by a podcast offering more insight and context from the authors. To see all the articles in the series, go to https://links.lww.com/AJN/A204 .

Quantitative research examines associations between research variables as measured through numerical analysis, where study effects (outcomes) are analyzed using statistical techniques. Such techniques include descriptive statistics (for example, sample mean and standard deviation) and inferential statistics, which uses the laws of probability to evaluate for statistically significant differences between sample groups (for example, t test, ANOVA, and regression analysis). Qualitative research explores research questions through an analysis of nonnumerical data sources (for example, text sources collected directly or indirectly by the researcher) and reports outcomes as themes or concepts that describe a phenomenon or experience.

As described in the first installment of this series, “a common goal of clinical research is to understand health and illness and to discover novel methods to detect, diagnose, treat, and prevent disease”; with this in mind, research questions must “focus on clear approaches to measuring or quantifying change or outcome,” the research outcome being the “planned measure to determine the effect of an intervention on the population under study.” 1

In this article, we explore measurement in quantitative research. We will also consider the concepts of validity and reliability as they relate to quantitative research measurement. Qualitative analysis will be considered separately in a future article in this series, as this methodology does not typically use a prescribed mechanism for measurement of research variables.

DEFINING THE VARIABLE OF INTEREST

Measurement in research begins with defining the variables of interest. Often, researchers are interested in exploring how variation in one factor or phenomenon influences variation in another. The dependent variables (outcome variables) in a study reflect the primary phenomenon of interest and the independent variables (or explanatory variables) reflect the factors that are hypothesized to have an impact on the primary phenomenon of interest (the dependent variable). 2 For example, a researcher might rightly hypothesize that body mass index (BMI) influences blood pressure, further hypothesizing that increases in BMI are associated with increases in blood pressure. In a study testing this hypothesis, blood pressure is the dependent variable and BMI is an independent variable.

In identifying the variables of interest in a study, researchers are likely to have ideas of concepts they would like to explore. For instance, among other things the researcher is interested in in the above example is weight. A conceptual definition of a research variable provides a general theoretical understanding of that variable; regarding weight, a person might be considered “thin” or “overweight.” Nevertheless, in moving from theory to practice, the researcher must consider how to operationalize this theoretical definition—that is, the researcher needs to select specific mechanisms for measuring the proscribed variables conceptualized in the study. Thus, an operational definition provides a measurable definition of a variable. Continuing with the above example, BMI would be a means of operationalizing the weight variable, where a person with a BMI of 25 or above is categorized by the Centers for Disease Control and Prevention as overweight. 3 In operationalizing variables, first look in existing evidence-based literature, practices, and professional guidelines. For instance, the researcher might consider measuring depression using the validated and widely utilized Patient Health Questionnaire-9 (PHQ-9) depression assessment scale or assessing longitudinal hyperglycemic risk by using the accepted measurement of glycated hemoglobin (HbA 1c ) level.

MEASUREMENT TOOLS

Researchers rely on measurement tools and instruments to create quantitative assessments of the variables studied. In some cases, direct measurements can be made using biometric measurement instruments to collect physiologic data such as weight, blood pressure, oxygen saturation level, and serum laboratory values. These biometric assessments are considered direct measures . 4 To quantify more abstract concepts, such as mood states, attitudes, and theoretical concepts like “caring,” researchers must consider less obvious proxy measures. Proxy measures constructed to quantify more abstract concepts are considered indirect measures . 4 For instance, Hughes developed an instrument to assess peer group caring during informal peer interactions among undergraduate nursing students. 5 While unable to directly measure the theoretical concept of caring, Hughes was able to construct an indirect proxy assessment using a survey tool.

Indirect, and even direct, measures can be operationalized in several ways. For instance, a researcher may consider operationalizing the concept of depression using the PHQ-9 depression assessment scale, using the Center for Epidemiological Studies Depression scale, or by applying Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition , diagnostic criteria. The study findings may be affected by how a variable is operationalized and which measurement tools are utilized; therefore, researchers should give serious thought to study objectives, sample/target populations, and other important considerations when operationalizing a variable. More specifics on measurement formats and methods of administration will be explored in the next installment of this series.

LEVELS OF MEASUREMENT

Levels of measurement describe the structure of a variable (see Table 1 ).

Nominal level . The lowest form of measurement, the nominal level groups data into distinct and exclusive categories that cannot be rank ordered. 2 Gender identity, race/ethnicity, occupation, geographic location, and clinical diagnoses are all examples of categories that contain nominal level data. This type of variable may also be referred to as a categorical variable. 6

Ordinal level data can also be categorized into distinct and exclusive groups; however, unlike nominal data, ordinal data can be ordered by rank. Likert-type scale variables reflect a classic example of ordinal level data, where responses can be rank ordered by “strongly disagree,” “disagree,” “neutral,” “agree,” and “strongly agree.” The 0-to-10-point pain scale is another example of the ordinal level of measurement. Using this scale, a patient provides a subjective determination of the experience of pain, where 0 reflects no pain and 10 reflects the highest pain threshold. As with all ordinal data, the precise quantitative distance between the descriptor data points is impossible to assess—the differences between a pain determination of 3 and one of 4 and a pain determination of 7 and one of 8 cannot be precisely calculated. Further, the distance between each pain level (for example, jumping from a pain level of 3 to 4 or from a pain level of 7 to 8) is not assumed to be incrementally or objectively equal. 2 Despite these drawbacks, ordinal level data are frequently translated into a numerical expression so they can be analyzed as interval or ratio data. For example, a Likert scale can be translated into a scale ranging from “strongly disagree = 1” to “strongly agree = 5,” allowing for the calculation of a numerical mean satisfaction score.

Interval level data reflect a chronological sequencing of data points with distances that are assumed to be quantifiable and equal in magnitude, such as ambient temperature. As with ambient temperature measured in degrees Fahrenheit, the magnitude of the chronological difference between each data point is assumed to be equal along a continuum of continuous values. Of note, interval data do not include a true and meaningful zero, the total absence of the characteristic being measured. 2 For example, there is no such thing as the absence of temperature.

Ratio level data provide the final and most robust level of measurement. Ratio level data are measured continuously, with equal spacing between intervals and with a true zero. Examples include height, weight, heart rate, and serum laboratory values. A zero value is interpreted as the absence of the characteristic. Once again, researchers should be cautious in defining how a variable is operationalized because the level of measurement will influence the types of statistical analyses that can be performed in the evaluation of study outcomes. Interval and ratio levels of measurement result in the most robust statistical analyses and research results. Statistical analysis techniques will be discussed in more detail later in this series.

MEASUREMENT ERROR

For variables to provide a meaningful and appropriate representation of the underlying concept being measured, data measurement needs to be accurate and precise. Measurement error reflects the difference between the measured and true value of the underlying concept. The value of an individual measurement can be described as follows 7 :

Chance error (or random error) changes from measurement to measurement, while bias (or systematic error) influences “all measurements the same way, pushing them in the same direction.” 7 Chance errors are individually unpredictable and inconsistent and in the long run should cancel each other out. If there is no bias in one's measurement, the individual and exact values should ultimately be equal. Bias, however, is inherent in all models and causes a systematic deviation from the true, underlying value.

Weight offers an excellent example of measurement error. Suppose some patients are weighed in the morning, some in the afternoon; some wear coats while others do not; some have eaten while others have fasted, and so forth. This variation reflects random error—we'd expect this positive and negative, over- and underestimation, to average out once enough patients have been sampled. Further, suppose the scale is incorrectly calibrated, such that it reports that every person weighs five pounds more than her or his actual weight. This result reflects a positive bias in the estimates and will not be corrected no matter how many patients are sampled.

The potential for bias resulting from measurement error falls broadly under the category of information bias —are researchers measuring what they think they are measuring? Information bias is present if the study data collected are somehow incorrect. 8 This can occur because of faulty measurement practices that systematically result in the under- or overvaluation of a measure, as described in the scale example above, or because of systematic misreporting by respondents. There are many forms of information bias, including recall bias, interviewer bias, and misclassification, as well as systematic differences in soliciting, recording, and interpreting information. 8 For instance, consider a study of adolescent sexual behavior in which adolescents are interviewed in the home with parents or guardians present. One might assume that adolescents in these circumstances would underreport the number of sexual partners they have had; as a result, one might expect this systematic underreporting to represent a downward bias in the data collected.

Other forms of bias exist, such as selection bias (if study participants are systematically different from the target population, or population of interest). Selection bias, for instance, does not necessarily affect the internal validity of the study (the ability to collect valid data) but may affect the external validity of the study (can researchers truly generalize findings to the population of interest?). These forms of bias will be described in further detail elsewhere in this series.

Measurement error is study and model specific; it comes in many forms and the type of error affects the level or form of bias. In interpreting results and designing research, researchers need to be aware of potential measurement error, do what they can to minimize bias, and provide a thorough assessment of bias in presenting the limitations of their work.

VALIDITY AND RELIABILITY

Validity refers to the degree to which a measurement accurately represents the underlying concept being measured. Basically, does the test operate as designed? Researchers need to consider the validity of use of the measurement instrument within the context of specific populations. For instance, in Hughes's study of caring among peer groups in undergraduate nursing populations, the author not only had to ensure that her instrument accurately measured caring among peer groups but also needed to verify that this measurement was accurate in nursing undergraduate populations. 5 In this instance, Hughes developed the survey explicitly with undergraduates in mind, making the second point easier to achieve.

Suppose, however, that a researcher wanted to use a version of the survey to gauge peer caring among nursing faculty. Would this be appropriate? Not without first assessing the validity of the survey within the new sample. The validity of an instrument can be assessed in several ways: by having the instrument reviewed by a content expert, comparing the instrument's results with those from an alternative assessment metric, assessing how well the instrument predicts current or future performance for the concept under consideration, and running a factor analysis (a statistical procedure that compares items or subscales within an instrument with each other and with the overall instrument outcome).

Reliability and validity go hand in hand. Reliability reflects the consistency of a measurement tool in reporting variable data. An instrument must be reliable to be valid. The reliability of an instrument can be gauged in three ways:

• stability (the consistency of outcomes with repeated implementation)
• interrater reliability (the consistency between different evaluators)
• internal consistency (the homogeneity of items within a scale as they relate to the measurement of the concept under investigation)

Cronbach α is a statistical procedure to assess instrument reliability by determining the internal consistency of items on a multi-item scale. 4, 9 Internal consistency evaluations examine how closely items on a scale represent the outcome concept under evaluation. Cronbach α scores range from 0.00 to 1.00—the higher the score the better the internal consistency. An acceptable Cronbach α as an evaluation of instrument reliability is often considered to be 0.70; however, a score of 0.80 or higher is preferable.

When choosing a measurement instrument for quantitative research, it is best to select one that has documented validity and reliability; alternatively, the researcher may independently complete and describe an assessment of the instrument's validity and reliability. Evaluation of a research study prior to practice implementation should also include assessment of the validity and reliability of the measurement instrument employed, which should be described within the research article.

This installment of AJN 's nursing research series explores how to measure both research outcomes and factors that are hypothesized to influence outcomes. Careful selection of measurement instruments will enhance the accuracy of research and maximize the ability of the research findings to meaningfully inform nursing practice and improve the well-being of patient populations. The next article in this series will further explore the selection and utilization of measurement instruments in the design and execution of nursing research.

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An Overview of the Fundamentals of Data Management, Analysis, and Interpretation in Quantitative Research

Affiliations.

• 1 Reader, School of Medicine, Dentistry & Nursing, University of Glasgow, Glasgow, Scotland, UK. Electronic address: [email protected].
• 2 Clinical Nurse Specialist, Department of Head and Neck and ENT Cancer Surgery of the Portuguese Institute of Oncology of Francisco Gentil, Lisbon, Portugal.
• 3 Senior Lecturer, School of Nursing and Midwifery, University of Galway, Galway, Ireland.
• 4 Associate Professor, Catalan Institute of Oncology and Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain.
• 5 Senior Nurse Scientist, Institute of Higher Education and Research in Healthcare (IUFRS), Faculty of Biology and Medicine, University of Lausanne, and Lausanne University Hospital, Lausanne, Switzerland.
• 6 Associate Professor, School of Nursing, Koc University, Istanbul, Turkey.
• 7 Clinical Nurse Specialist, Department of Gastrointestinal Surgery, Cancer Center, Ghent University Hospital, Ghent, Belgium.
• 8 Associate Professor, School of Nursing, Psychotherapy and Community Health, Dublin City University, Dublin, Ireland.
• 9 Reader, School of Nursing, Institute of Nursing and Health Research, Ulster University, Belfast, UK.
• 10 Professor, Department of Clinical Research, University of Southern Denmark, Department of Oncology, Odense University Hospital, Odense, Denmark.
• 11 Reader, School of Health and Life Sciences, University of the West of Scotland, South Lanarkshire, Scotland, UK.
• PMID: 36868925
• DOI: 10.1016/j.soncn.2023.151398

Objectives: To provide an overview of three consecutive stages involved in the processing of quantitative research data (ie, data management, analysis, and interpretation) with the aid of practical examples to foster enhanced understanding.

Data sources: Published scientific articles, research textbooks, and expert advice were used.

Conclusion: Typically, a considerable amount of numerical research data is collected that require analysis. On entry into a data set, data must be carefully checked for errors and missing values, and then variables must be defined and coded as part of data management. Quantitative data analysis involves the use of statistics. Descriptive statistics help summarize the variables in a data set to show what is typical for a sample. Measures of central tendency (ie, mean, median, mode), measures of spread (standard deviation), and parameter estimation measures (confidence intervals) may be calculated. Inferential statistics aid in testing hypotheses about whether or not a hypothesized effect, relationship, or difference is likely true. Inferential statistical tests produce a value for probability, the P value. The P value informs about whether an effect, relationship, or difference might exist in reality. Crucially, it must be accompanied by a measure of magnitude (effect size) to help interpret how small or large this effect, relationship, or difference is. Effect sizes provide key information for clinical decision-making in health care.

Implications for nursing practice: Developing capacity in the management, analysis, and interpretation of quantitative research data can have a multifaceted impact in enhancing nurses' confidence in understanding, evaluating, and applying quantitative evidence in cancer nursing practice.

Keywords: Data analysis; Data management; Empirical research; Interpretation; Quantitative studies; Statistics.

Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.

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• Volume 17, Issue 1
• Qualitative data analysis: a practical example
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• Helen Noble 1 ,
• Joanna Smith 2
• 1 School of Nursing and Midwifery, Queens's University Belfast , Belfast , UK
• 2 Department of Health Sciences , University of Huddersfield , Huddersfield , UK
• Correspondence to : Dr Helen Noble School of Nursing and Midwifery, Queen's University Belfast, Medical Biology Centre, 97 Lisburn Road, Belfast BT9 7BL, UK; helen.noble{at}qub.ac.uk

https://doi.org/10.1136/eb-2013-101603

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The aim of this paper is to equip readers with an understanding of the principles of qualitative data analysis and offer a practical example of how analysis might be undertaken in an interview-based study.

What is qualitative data analysis?

What are the approaches in undertaking qualitative data analysis.

Although qualitative data analysis is inductive and focuses on meaning, approaches in analysing data are diverse with different purposes and ontological (concerned with the nature of being) and epistemological (knowledge and understanding) underpinnings. 2 Identifying an appropriate approach in analysing qualitative data analysis to meet the aim of a study can be challenging. One way to understand qualitative data analysis is to consider the processes involved. 3 Approaches can be divided into four broad groups: quasistatistical approaches such as content analysis; the use of frameworks or matrices such as a framework approach and thematic analysis; interpretative approaches that include interpretative phenomenological analysis and grounded theory; and sociolinguistic approaches such as discourse analysis and conversation analysis. However, there are commonalities across approaches. Data analysis is an interactive process, where data are systematically searched and analysed in order to provide an illuminating description of phenomena; for example, the experience of carers supporting dying patients with renal disease 4 or student nurses’ experiences following assignment referral. 5 Data analysis is an iterative or recurring process, essential to the creativity of the analysis, development of ideas, clarifying meaning and the reworking of concepts as new insights ‘emerge’ or are identified in the data.

Do you need data software packages when analysing qualitative data?

Qualitative data software packages are not a prerequisite for undertaking qualitative analysis but a range of programmes are available that can assist the qualitative researcher. Software programmes vary in design and application but can be divided into text retrievers, code and retrieve packages and theory builders. 6 NVivo and NUD*IST are widely used because they have sophisticated code and retrieve functions and modelling capabilities, which speed up the process of managing large data sets and data retrieval. Repetitions within data can be quantified and memos and hyperlinks attached to data. Analytical processes can be mapped and tracked and linkages across data visualised leading to theory development. 6 Disadvantages of using qualitative data software packages include the complexity of the software and some programmes are not compatible with standard text format. Extensive coding and categorising can result in data becoming unmanageable and researchers may find visualising data on screen inhibits conceptualisation of the data.

How do you begin analysing qualitative data?

Despite the diversity of qualitative methods, the subsequent analysis is based on a common set of principles and for interview data includes: transcribing the interviews; immersing oneself within the data to gain detailed insights into the phenomena being explored; developing a data coding system; and linking codes or units of data to form overarching themes/concepts, which may lead to the development of theory. 2 Identifying recurring and significant themes, whereby data are methodically searched to identify patterns in order to provide an illuminating description of a phenomenon, is a central skill in undertaking qualitative data analysis. Table 1 contains an extract of data taken from a research study which included interviews with carers of people with end-stage renal disease managed without dialysis. The extract is taken from a carer who is trying to understand why her mother was not offered dialysis. The first stage of data analysis involves the process of initial coding, whereby each line of the data is considered to identify keywords or phrases; these are sometimes known as in vivo codes (highlighted) because they retain participants’ words.

• View inline

Data extract containing units of data and line-by-line coding

When transcripts have been broken down into manageable sections, the researcher sorts and sifts them, searching for types, classes, sequences, processes, patterns or wholes. The next stage of data analysis involves bringing similar categories together into broader themes. Table 2 provides an example of the early development of codes and categories and how these link to form broad initial themes.

Development of initial themes from descriptive codes

Table 3 presents an example of further category development leading to final themes which link to an overarching concept.

Development of final themes and overarching concept

How do qualitative researchers ensure data analysis procedures are transparent and robust?

In congruence with quantitative researchers, ensuring qualitative studies are methodologically robust is essential. Qualitative researchers need to be explicit in describing how and why they undertook the research. However, qualitative research is criticised for lacking transparency in relation to the analytical processes employed, which hinders the ability of the reader to critically appraise study findings. 7 In the three tables presented the progress from units of data to coding to theme development is illustrated. ‘Not involved in treatment decisions’ appears in each table and informs one of the final themes. Documenting the movement from units of data to final themes allows for transparency of data analysis. Although other researchers may interpret the data differently, appreciating and understanding how the themes were developed is an essential part of demonstrating the robustness of the findings. Qualitative researchers must demonstrate rigour, associated with openness, relevance to practice and congruence of the methodological approch. 2 In summary qualitative research is complex in that it produces large amounts of data and analysis is time consuming and complex. High-quality data analysis requires a researcher with expertise, vision and veracity.

• Cheater F ,
• Robshaw M ,
• McLafferty E ,
• Maggs-Rapport F

Competing interests None.

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Quantitative Data Analysis

Resources for study design through to data analysis and dissemination

Statistical analysis of quantitative data requires first choosing an appropriate method . Following analysis, results are summarized and reported in tables, charts, and graphs for interpretation, discussion, and dissemination in papers, manuscripts, and/or presentations. 

UCalgary's  Data Science Advisory Unit  is available to help with study design, statistical analysis, visualization, and interpreting results (fee may apply).

Getting Started

Types of data, descriptive statistics, hypothesis testing, sample size & power, interpreting results, reporting results.

Designing a study includes developing good research question(s), choosing an appropriate methodology, estimating sample size, selecting data collection tools, and creating an analysis plan. 

UCalgary's Research Computing Services is available to help researchers with study design, interpretation of results, and writing up results for publication.

Research Questions & Study Design

Your study design is guided by the research question(s). For example, does your question start with “How?” or “Why?” If so, your questions might be better addressed using qualitative methods. If you are asking “What?”, “When?, “Where?” or “How much?” you could consider quantitative methodologies. You might even combine both into a mixed methods approach.

Example Study Designs

• Descriptive (case reports, case series, descriptive surveys)
• Observational/Analytic (cross-sectional, case-control, cohort, hybrid)
• Experimental/Intervention (randomized controlled trials, quasi-experimental designs)
• Mixed Methods (quantitative and qualitative methodologies combined)

Sample Size & Power Calculations

Sample size calculations are a key part of a research study. Sample size calculations should be run for each of your main/primary outcomes so you know that your study won’t be underpowered for any of the questions you plan to address.

The sample size calculation depends on your hypothesis test, the significance level (usually set as 5%), and the power and results from your pilot study. There are many formulas available for different research situations. 

Data Collection & Sampling

Another important part of study planning is selecting a sampling technique. How will you select your participants? Will it be a convenience sample? Random sample? Cluster sample? Snowball sample?

Your choice will depend on the research question(s) and study design. Note that different sampling methods have corresponding potential biases. For example, if your sample was a “convenience sample,” the results may not be generalizable or may be biased in other ways (e.g. selection bias).

Analysis Plan

The analysis plan is your road map for data management and analysis. Writing up an analysis plan is a great way to keep things on track.

Conducting a complete analysis of the data you have collected will enable you to:

• Answer your research question(s)
• Determine the impact of your work
• Have scientific validation of your work

Plans also help with timelines and standardizing analytic approaches (e.g. treatment of missing values, inclusion and exclusion criteria). 

Data Entry, Coding, & Cleaning

Data will have to be entered, coded and checked, new variables created, etc., even when doing secondary data analyses.

Create a data dictionary containing all of your variables, any derived variables and notes about how you coded them. This is useful not only if you will be sharing the dataset with other, but for yourself (e.g., if you need to come back to the data after some time away). Keep in mind that data cleaning is a process that will likely involve you revisiting the data several times over.

Primary data :  data that you collect yourself from participants or health records using surveys, chart reviews, interviews, focus groups, etc.

Secondary data :  data collected by other researchers that you are using to answer your own research questions

Qualitative vs. Quantitative Data

The type of data you collect depends on the question you want to answer and your resources. Both quantitative and qualitative data have strengths and limitations and may be appropriate for different settings, evaluation designs, and evaluation questions.

Qualitative data consist of words and narratives. The analysis of qualitative data can come in many forms including highlighting key words, extracting themes, and elaborating on concepts.

Quantitative data are numerical information, the analysis of which involves statistical techniques. The type of data you collect guides the analysis process.

Types of Variables

Dependent (Outcome) variables are the outcome of interest and will answer your research question(s).

Independent (Predictor) variables  are those factors that may influence your dependent variable/outcome variable.

Example: Say you’re conducting a study on diet and exercise. Your weight would be your dependent variable and your diet and exercise (which both influence weight) would be your independent variables.

Categorical vs. Continuous Variables

Categorical variables are based on groupings or classification. There are two types: Nominal (no inherent order) and Ordinal (natural order).

Nominal Example – Smoker vs. Non-Smoker

Ordinal Example – Educational Level (Less than High School, High School, Some College, College, Bachelor’s Degree, Graduate Degree)

Continuous variables can take on any score or value within a measurement scale. There are two types: Interval and Ratio Scale . An interval variable can be ordered, and the distance or level between each category is equal and static. A ratio scale variable is similar to an interval variable with one difference: the ratio scale has true zero point (i.e., 0.0 = none/absence of the measurement).

Interval Example –  Temperature

Ratio Scale Example – Weight

Descriptive statistics are commonly used to describe and explore quantitative datasets.

Before proceeding, you should assess the distribution of your data and consider variable transformations or non-parametric options, if necessary. It’s also a good idea to identify missing data and start thinking about how you might want to handle this (e.g. listwise deletion, imputation).

Common Descriptive Statistics

• Minimum (Min): lowest/smallest score in a data set
• Maximum (Max): highest/largest score in a data set
• Frequency : number of times a certain score appears in a data set
• Mean (Average): sum of all the scores divided by the number of scores
• Median : middle score of a data set after values ordered numerically; it divides the distribution in half
• Mode : most frequently occurring score in a data set
• Standard Deviation (SD): represents the average amount that a given score deviates from the mean score

Data and Normality

The goal of estimation and hypothesis testing is to generalize the results from a sample to the population. We need to determine whether a pattern we observe in the sample is due to chance or due to program or intervention effects.

Inferential analysis is used to determine if there is a relationship between an intervention or program and an outcome, as well as the strength of that relationship. The type of test selected for inferential analysis should be guided by the distribution of your data. Is it a normal or non-normal distribution?   

Normal Distribution

A normal distribution looks like a Bell Curve (right).

Looking at your distribution, draw a curve over it that most closely fits your data. If your curve closely resembles the one in the image, your distribution is normal.

In a normal distribution the majority of the data is clustered around one number or value. If the data is normal, we usually choose a parametric statistical test for data analysis.

Non-Normal Distributions

There are several reasons that a distribution may be non-normal. A small sample size or unusual sets of responses are common reasons that data may not be normally distributed. If the data is non-normal, we usually choose from a set of statistical tests called non-parametric statistical tests .

Non-normal data will have issues with skewness and/or kurtosis (below).

Negative Skew

The graph shows negatively skewed data; the majority of the scores are at the higher end of possible scores. The curve has a longer curve to the left.

Positive Skew

The graph shows positively skewed data; the majority of the scores are at the lower end of possible scores. The curve has a longer curve to the right.

This graph illustrates kurtosis, the spread or ‘peakedness’ of the distribution. A distribution can be too peaked or pointy.

Choosing a Statistical Test

Statistical tests allow us to make inferences about a sample because they can validate if the differences, associations, and patterns that we detect are real and not due to chance.

Selecting the appropriate test depends on the research design, the type of variable, and the distribution of the data.

If the data is normally distributed, you will choose a type of  parametric test. If normality is violated, then you will need to use a test that doesn’t need the normality assumption to be valid. We call these non-parametric   tests or parametric-independent tests.

SAGE Research provides an online tool to help you decide which test to choose.

Sample Size & Power

Sample size calculations should be run for each of your primary outcomes so that your study won't be underpowered for any of the research questions that you plan to address. *For surveys, Qualtrics has a sample size calculator .

Before calculating sample size, ask yourself:

1. Is my study descriptive or comparative ?

2.  Is(are) the primary outcome variable(s)  continuous  or  categorical ?

Descriptive Studies

Use the Confidence Interval Approach

Use this approach to estimate your sample size when you want an interval around an estimate with a certain confidence level.

The population prevalence of hypertension among Canadians aged 20 to 79 was found to be significantly higher for men (24.5%, 95% CI : 22.7% to 26.4%) than for women (21.5%, 95% CI : 19.8% to 23.2%).  Statistics Canada

Therefore, for men, point estimate is 24.5%, the margin of error is 1.9%.

Comparative Studies

Use the Hypothesis Testing Approach

Use this approach to estimate your sample size so that if such a difference exists, then findings would be statistically significant . The information you need to calculate a sample size will vary according to your study design, research questions, analysis plan and study restrictions. Prior to the calculation, you will need to decide on your:

Power . This is the ability of the statistical test to detect differences or effects that lead to rejection of the null hypothesis. It depends on the sample size. The larger the sample size, the bigger the power. It is important to calculate the sample size to have sufficient power before you begin your data collection. When your sample size is small, your study might not be able to detect the difference or effect, even when it is real, because of lack of power.  Power is usually set at 80-90% power.

Level of significance (α). This is the pre-set level of error that you want to commit in your research, determined before your data collection. It is usually set at 0.05 or 0.01. P-value is the actual level of error found when you perform the statistical test. When p-value < α, then it supports the evidence against the null hypothesis (no effect) and your results are ‘statistically significant’.

In the results section you report on just the objective “facts and figures” of what you found. You then interpret these results in the discussion section .

1. Discuss the results in relation to each hypothesis

You have to report the results of your project or study in relation to your research questions/hypothesis. Present the results of the outcome variable(s) for each hypothesis.

2. Explain your results

Some guiding questions to consider when explaining your results:

• Do the results agree with the ideas that you introduced in your proposal?
• How do the results relate to previous literature or current theory?
• Discuss any of the limitations in the study design that may reduce the strength of your results.

3. Interpret p-values

Use descriptive language to indicate the strength of the evidence.

p-value , Description

< 0.001, Extremely significant ; Very strong evidence against the null hypothesis in favor of the alternative

0.001 – 0.010, Highly significant ; Strong evidence against the null hypothesis in favor of the alternative

0.011 – 0.050, Significant ; Moderate evidence against the null hypothesis in favor of the alternative

0.051 – 0.100, Not significant ; Weak evidence against the null hypothesis in favor of the alternative

> 0.100, No evidence ; No evidence against the null hypothesis

4. Generalize your results

This is where you explain the extent to which your study is externally valid . Discuss strengths and weaknesses of applying your results to, e.g., another population, species, age, or sex.

5. Propose a plan of action for future research

Based on your results, and considering the study's limitations, introduce new ideas or ways to improve the current area of research.

6. If your results were unexpected, discuss possible explanations

Try to identify and discuss factors or conditions that may have contributed to unexpected results. For example, site conditions (e.g., room temperature) could have been different between two focus group sessions. 

7. Do not overstate the importance of your findings

Be careful about drawing erroneous conclusions. Report only actual findings and the relationships and associations between outcomes and predictor variables that have been confirmed with statistical evidence. For example, just because you find that two variables are related, you cannot automatically leap to the conclusion that those two variables have a cause-and-effect relationship.

8. Avoid speculating beyond the data

Refrain from generalizing your results to a larger group than was actually represented by your study. For example, results from a study involving nursing students may not be applicable to registered nurses.

9. Remain focused on the research question(s)

Resist the temptation to deviate or to make sweeping generalities based on your findings.

10. End the discussion on a positive note

Summarize the study’s strengths, conclusions, implications, and your suggestions for future research.

The Results  section of your paper should only be used to report, not interpret, your findings. The Discussion  section is where the interpretation and implications of your findings are presented.

The American Psychological Association ( APA ) style guide is most commonly used within the social sciences. 

Best Practices for Reporting

1. Summarize succinctly.

The Results section is the shortest and most condensed section in a manuscript or thesis/dissertation, typically 1-2 pages. Present each of your variables in separate subsections, writing a brief summary for each.

2. Keep the 'Results' and 'Discussion' sections separate.

Statistical results are presented but are not discussed in the results section . Reserve your interpretation for the discussion section .

3. Provide the results separately for each hypotheses.

The Results section should describe how your data supports or refutes each hypothesis.

4. Include tables and figures.

Using tables and figures is a great way to summarize your results. Include descriptive statistics (such as means and standard deviations) and/or the results of any inferential statistics (test statistic, degrees of freedom, confidence intervals, and the p-value).

5. Be careful when drawing conclusions.

Draw appropriate conclusions from your findings. Do not overstate the importance of results and limit your conclusions to the population that is actually represented by your study.

Frequently Asked Questions

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Use of Research in the Nursing Practice: from Statistical Significance to Clinical Significance

Uso de la investigación en la práctica de enfermería: de la significancia estadística a la significancia clínica, utilização da pesquisa na prática de enfermagem: da significância estatística à significância clínica, r. mauricio barría p..

1 RN, M.Sc, Ph.D. Director of the Institute of Nursing, Faculty of Medicine, Universidad Austral de Chile. email: [email protected] , Universidad Austral de Chile, Faculty of Medicine, Universidad Austral de Chile, Chile, lc.hcau@airrabr

Within the context of evidence-based practice, this article exposes the reflection on the understanding and usefulness of the information provided by the research findings shared in reports and research publications, exposing differences based on the interpretation of statistical significance and clinical significance.

Content synthesis.

Basic aspects of the meaning and use of the information reported by research on p value (statistical significance) and the value and usefulness of these results are analyzed and exemplified, contrasting the value for the practice of an additional judgment on clinical significance. In addition to establishing conceptual differences, the need is highlighted for nurses to have the competencies to differentiate and apply each of them according to the clinical contexts of their potential implementation.

Conclusion.

The real usefulness of research about interventions within the context of nursing care is given by its real application and reach for the practice and benefit for patients. For this to occur, nurses must interpret adequately the information provided by scientific publications and other research reports.

En el contexto de una práctica basada en evidencia, este artículo expone la reflexión sobre la comprensión y utilidad de la información que proveen los hallazgos de investigación reportados en informes y publicaciones de investigación, exponiendo las diferencias a partir de la interpretación de la significancia estadística y significancia clínica.

Síntesis del contenido.

Se analizan y ejemplifican aspectos básicos sobre el significado y uso de la información que reportan las investigaciones sobre valor p (significancia estadística) y el valor y utilidad de estos resultados contrastando el valor para la práctica de un juicio adicional sobre significancia clínica. Además de establecer diferencias conceptuales, se resalta la necesidad de que las enfermeras tengan las competencias para diferenciar y aplicar cada uno de ellos según los contextos clínicos de su potencial implementación.

Conclusión.

La real utilidad de la investigación sobre intervenciones en el contexto del cuidado de enfermería está dada por su real aplicación y alcance para la práctica y el beneficio para los pacientes. Para que ello ocurra, las enfermeras deben interpretar adecuadamente la información que proveen las publicaciones científicas y otros reportes de investigación.

No contexto de uma prática baseada em evidências, este artigo apresenta a reflexão sobre a compreensão e utilidade da informação fornecida pelos resultados da investigação relatados em relatórios de investigação e publicações, expondo as diferenças com base na interpretação da significância estatística e da significância clínica.

Síntese de conteúdo.

Aspectos básicos sobre o significado e uso das informações relatadas pelas pesquisas sobre valor p (significância estatística) e o valor e utilidade desses resultados são analisados ​​e exemplificados, contrastando o valor para a prática de um julgamento adicional sobre significância clínica. Além de estabelecer diferenças conceituais, destaca-se a necessidade de o enfermeiro ter competências para diferenciar e aplicar cada uma delas de acordo com os contextos clínicos de seu potencial implementação.

Conclusão.

A real utilidade da investigação sobre intervenções no contexto dos cuidados de enfermagem é dada pela sua real aplicação e âmbito de prática e benefício para os pacientes. Para que isso ocorra, os enfermeiros devem interpretar adequadamente as informações fornecidas pelas publicações científicas e outros relatórios de pesquisa.

Introduction

Daily, nurses face dilemmas in clinical practice to make decisions about caring for patients, a situation in which research contributes to the scientific rigor of daily practice, allowing improvements when applying knowledge in favor of caring for patients. 1 Thus, the use of research in nursing practice is fundamental to provide quality and evidence-based care. Nursing research began with Nightingale when she investigated the morbidity and mortality of patients during the Crimean War. From there, it was again taken up until the 1930s and 1940s when nurses began to conduct studies on nursing education. During the 1950s and 1960s, nurses and nursing roles were the focus of research, until the end of the 1970s and 1980s, the aim of research centered on studies to improve the nursing practice. In the 1990s, research sought to describe nursing phenomena, test the effectiveness of nursing interventions, and examine the results on patients. Currently, nursing research of the 21 st century considers quality studies through the use of a variety of methodologies, synthesis of research findings, use of this evidence to guide the practice and examine the results of the evidence-based practice. 2

A key aspect of using research in the nursing practice is the application of scientific evidence on clinical decision making. When basing decisions on the best evidence available, nurses can provide more effective and safe care, but this requires reviewing and critically evaluating published studies, considering their validity, relevance and applicability to the specific clinical situation. It is within this context that it is proven that sufficient knowledge is still not available on the part of clinicians to adequately evaluate the research findings to be translated into practice. Examples of this are the assessments of the concepts of statistical significance and clinical significance. In the nursing field, clinical significance and statistical significance are fundamental concepts in evidence-based decision making. These concepts permit evaluating the relevance of research results and their application in the clinical practice. Although both terms are related, it is important to comprehend their differences and how they complement each other to provide quality care to patients.

In quantitative research, nurse researchers are expected to assess, understand, and report the results of their studies using appropriate statistical methods, as well as provide a description of the clinical relevance of their findings to make sure an article is not just a description of new knowledge, but that it is useful for evidence-based practice. A focus on the magnitude of the effects, rather than simply their statistical significance (p value), could provide the opportunity to link data generated in each study with the clinical relevance these could provide. Reaching this statistical comprehension in the nursing practice will improve directly or indirectly the research articles and will facilitate communication between statisticians and clinical professionals to improve the reporting of research and disseminating findings. However, it is difficult to expect for all nurses to be experts in statistics and, additionally, to ensure that statisticians have the vision and clinical knowledge, so a dialogue must be achieved between both visions. 3

It is common to read research reports (publications) or see presentations of scientific sessions and conferences in which researchers, when reporting on comparisons of therapeutic or preventive interventions, use the expressions "statistical significance" or "statistically significant". This entails the danger of confusing clinical and statistical significance. Although, traditionally, reports of research results focus heavily on statistical significance, numerous errors have been noted when using this as the only approach to interpret and apply research findings. Furthermore, some warn that decisions should never be made based only on a significance test or p value and that in reality p values continue to be poorly understood and widely misused. 4 In the expression of statistical analyses, undoubtedly, the most universally recognized is the p value. Most people have the notion that a p value < 0.05 means a statistically significant difference among groups being compared. However, the traditional interpretation of statistical significance as p < 0.05 is arbitrary and errors have been observed in its interpretation, besides, it is expected that they will change according to the sample size, observing that bigger samples provide results with smaller p values. 5 This article sought to provide basic and conceptual information about the implications of the terms statistical significance and clinical significance and make people reflect on the understanding and usefulness of the information provided by research findings shared in reports and research publications.

Statistical significance

Overall, it could be understood that statistical significance is a term indicating that the results obtained in an analysis of data from a sample are unlikely to be due to chance at some specific level of probability, given the veracity of a null hypothesis. Thus, a p value represents the probability of calculating a statistical test from the data from a sample ( e.g ., a mean difference between two groups) that is equal to or more extreme than that observed in the sample data assuming that the null hypothesis is actually true. In other words, the p value measures how compatible the data from the sample is with the null hypothesis ( e.g ., there are no differences between the groups). 6 - 8

Significance tests have become an integral part of the process of quantitative research in scientific disciplines, including nursing. These tests complement the scientific method and offer an objective dimension in the analysis of studies to answer questions from the practice. Studies use a predefined threshold to determine when a p value is small enough to support a hypothesis in the study. Conventionally, this threshold is set at a p value of 0.05, equivalent to a type-I error probability level (alpha level or p ) of 5% and whose determination is achieved through hypothesis tests. However, there may be situations and justifications for studies to use a different threshold, if appropriate.

As outlined, researchers typically develop two types of hypotheses, a null hypothesis (H 0 ) and an alternative hypothesis (H 1 ). The null hypothesis establishes that no relation exists (or there is no difference) among groups in the study of variables of interest and any relationship that can be observed is due to chance or sampling fluctuations. The alternative hypothesis affirms that a relation or difference exists, which is not due to chance and is assumed real (example in Table 1 ).

It should be mentioned that, in studies using a sufficiently large sample, a statistical test almost always will demonstrate a significant difference, unless there is no effect at all, that is, when the effect size is exactly zero. Furthermore, very small differences, even being significant, often make no sense and do not provide value or utility for the practice. Therefore, reporting only the significant p value for an analysis is not adequate for readers to fully understand the results. 8 As reinforced by Polit, 9 an important reason for not homologating statistical significance with clinical significance is precisely because statistical significance is strongly affected by sample size and, thus, in a study with a large sample, the statistical power is high and the risk of committing a type-I error (erroneously concluding that no relationship exists among the variables) is low. Polit exemplifies it thus, “…with a sample size of 500, a modest correlation of r = 0.10 is statistically significant at p < 0.05, even though such a weak relationship may have little practical importance”. (9, p.18)

Clinical significance

Given that no universal agreement exists on the definition of clinical significance, various approaches exist for its evaluation. In addition, it has not received sufficient attention in the specific nursing literature reflecting that recent progress in measuring the clinical importance have not penetrated to a large extent in nursing. 9 It is even described that its use has been carried out inconsistently and without always considering a measurable result for the patient. 10 Overall, clinical significance refers to the practical importance of a result in real life or the benefits of research results for users and patients. It often measures the magnitude of the relation between an independent variable and an outcome variable. As expressed, conceptually, the importance of clinical significance is illustrated in its comparison with statistical significance. This is that, while the p values of a statistically significant finding indicate the probability that a change is caused by chance, clinical significance establishes whether this change or difference is large enough to have implications in practice. As anticipated, it is recognized that a p value cannot express the clinical relevance or importance of the effects observed from an intervention and specifically, does not provide details on the magnitude of an effect. So, although a p value is significant (conventionally < 0.05), it is possible that the difference between groups is small. This phenomenon is especially common with larger samples in which comparisons can yield as a result statistically significant differences that are actually not clinically significant. 10

As proposed by Bruner et al. , 10 numerous problems exist associated with using clinical significance in the nursing literature. Among these, they highlight the lack of consensus on the use of the term from a multiplicity of opinions, definitions, and uses. In turn, given that clinical significance is commonly based on the researcher’s judgement, the term is sometimes used subjectively and the findings are prone to bias in favor of positive results. Lastly, most studies do not incorporate the patient’s perspective. Thus, it is necessary to highlight that besides this vision from the clinician’s perspective, there are proposals that have been gaining space in the assessment of research and its applicability in the practice and which is guided from the very patient’s perspective, such as the concept of minimal clinically important difference. 11

Application of statistical significance and clinical significance

To illustrate the relation between statistical and clinical significance, let us consider the fictitious scenario in which a group of research nurses studies a breastfeeding support program to reduce early abandonment of breastfeeding after hospital discharge ( Table 1 ). Supposing that the result or outcome is measured in a binary scale, like maintains/abandons breastfeeding, at the end of the study, a significant difference is reported on the proportion of abandonment of breastfeeding between both groups. Although this result indicates that the difference between the study groups is probably not due to chance, it only provides partial information, given that, strictly speaking, statistical significance has not proven anything. When a result is deemed statistically significant, it is understood that an independent variable has an effect upon a dependent variable but does not prove that something will occur, given that the p value does not express magnitude.

It is necessary to know whether or not this finding, in addition to being a statistically significant difference, has any clinical value. Reviewing the results, it is confirmed that abandonment of breastfeeding one month after hospital discharge in the experimental group was 20%, while in the control group it was 60% ( Figure 1 ). This drastic reduction in abandonment of breastfeeding in the experimental group could be considered relevant given the known benefits of breastmilk in different settings, both for the mother and child, which supposes that the potential population benefitted would justify implementing a program within the hospital context, like the one studied. Additionally, the researchers have reported a p value = 0.045, which under the conventional assumption of the limit value assigned to it of 5% (0.05), also corroborates statistical significance.

Now, let's suppose another scenario where in a similar proposal researchers recruited more participants for their research, obtaining a result that highlighted that in the experimental group abandonment of breastfeeding one month after discharge was 48% while in the control group it was 52%. Although the statistical significance reported by the researchers, given the p value = 0.028, indicates statistically significant differences between the groups, it is necessary to consider whether the merely 4% reduction in the outcome studied justifies implementing an individualized breastfeeding support program. Thereby, researchers and readers of the research report will have evidence to discuss carefully this statistically significant finding, highlighting the apparently marginal clinical importance of the resources required to implement the intervention. Further, in comparing the examples mentioned, differences in p values obtained are expressed given the influence of the also different sample sizes. In this case, it is noted that although the p value from the example in Figure 2 is lower than that in Figure 1 (0.028 Vs. 0.045), clarifying that a smaller p value does not necessarily guarantee clinical significance.

Hence, as reflected in this example, the clinical significance of the research results is best evaluated by making a judgment based on clinical experience, assessing the benefits, costs, and risks associated with the findings of each study. If the benefits (or effects) reported clearly outweigh the risks and the effect is large enough, then a statistically significant finding is also clinically significant.

To end, it is worth mentioning that besides a purely qualitative view of how large or small a difference or effect is found in the results of a study, the size of the effect is estimated with different indices. Overall, a difference exists between those analyzing effect sizes between groups and those analyzing measures of association between variables. For two independent groups, the size of the effect can be measured through the standardized difference between to measurements. Cohen’s d term is an index of the size of the effect and classifies it into small (d=0.2), medium (d=0.5), and large (d=0.8) effect sizes. 8 Readers should delve into this and other concepts of effect size measurements.

This article has sought to reflect on the need for clinical nurses, as well as those who use research findings for their potential application, to expand the evaluation of study results beyond the merely statistical evaluation and contrast this information with clinical usefulness and its impact on patients and population. It is clear that exclusive dependence on statistical significance to assign meaning and importance to research findings continues being a problem in different areas of health sciences and in nursing. Upon contrasting conceptually, the scope of the terms statistical significance and clinical significance, it is expected that evidence-based decisions will be made cautiously, understanding that statistical significance allows inferences to be made about the results of a study, but this is not sufficient to make sound recommendations about the potential clinical benefits from those findings. Consequently, researchers and clinicians need to always assess the clinical importance of the research findings and weigh statistically significant results within the context of their importance for the practice and benefit in patients.

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Delphi technique on nursing competence studies: a scoping review.

1. Introduction

1.1. the delphi technique, 1.1.1. selection and composition of the expert’s panel, 1.1.2. rounds, 1.1.3. data analysis and consensus, 1.1.4. reliability and validity, 1.1.5. advantages and disadvantages of the delphi technique, 1.2. rationale, context and aim of the scoping review, 2. materials and methods, 2.1. eligibility criteria, 2.2. search strategy, 2.3. study selection, 2.4. data extraction and presentation, 3.1. preparatory procedures, 3.2. access and expert selection procedures, 3.3. acquisition of experts’ inputs, 3.3.1. instrumentation, 3.3.2. first round, 3.3.3. subsequent rounds, 3.3.4. stability of the expert panel, 3.4. data analysis and consensus, 3.5. ethical–legal procedures and guarantees, 4. discussion, limitations, 5. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, conflicts of interest.

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Furtado, L.; Coelho, F.; Pina, S.; Ganito, C.; Araújo, B.; Ferrito, C. Delphi Technique on Nursing Competence Studies: A Scoping Review. Healthcare 2024 , 12 , 1757. https://doi.org/10.3390/healthcare12171757

Furtado L, Coelho F, Pina S, Ganito C, Araújo B, Ferrito C. Delphi Technique on Nursing Competence Studies: A Scoping Review. Healthcare . 2024; 12(17):1757. https://doi.org/10.3390/healthcare12171757

Furtado, Luís, Fábio Coelho, Sara Pina, Cátia Ganito, Beatriz Araújo, and Cândida Ferrito. 2024. "Delphi Technique on Nursing Competence Studies: A Scoping Review" Healthcare 12, no. 17: 1757. https://doi.org/10.3390/healthcare12171757

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• Systematic Review
• Open access
• Published: 05 September 2024

Exploring the use of social network analysis methods in process improvement within healthcare organizations: a scoping review

• Troy Francis 1 , 2 , 3 ,
• Morgan Davidson 1 ,
• Laura Senese 1 ,
• Lianne Jeffs 1 ,
• Reza Yousefi-Nooraie 4 ,
• Mathieu Ouimet 5 ,
• Valeria Rac 1 , 3   na1 &
• Patricia Trbovich 1 , 2   na1  

BMC Health Services Research volume  24 , Article number:  1030 ( 2024 ) Cite this article

Metrics details

Communication breakdowns among healthcare providers have been identified as a significant cause of preventable adverse events, including harm to patients. A large proportion of studies investigating communication in healthcare organizations lack the necessary understanding of social networks to make meaningful improvements. Process Improvement in healthcare (systematic approach of identifying, analyzing, and enhancing workflows) is needed to improve quality and patient safety. This review aimed to characterize the use of SNA methods in Process Improvement within healthcare organizations.

Relevant studies were identified through a systematic search of seven databases from inception - October 2022. No limits were placed on study design or language. The reviewers independently charted data from eligible full-text studies using a standardized data abstraction form and resolved discrepancies by consensus. The abstracted information was synthesized quantitatively and narratively.

Upon full-text review, 38 unique articles were included. Most studies were published between 2015 and 2021 (26, 68%). Studies focused primarily on physicians and nursing staff. The majority of identified studies were descriptive and cross-sectional, with 5 studies using longitudinal experimental study designs. SNA studies in healthcare focusing on process improvement spanned three themes: Organizational structure (e.g., hierarchical structures, professional boundaries, geographical dispersion, technology limitations that impact communication and collaboration), team performance (e.g., communication patterns and information flow among providers., and influential actors (e.g., key individuals or roles within healthcare teams who serve as central connectors or influencers in communication and decision-making processes).

Conclusions

SNA methods can characterize Process Improvement through mapping, quantifying, and visualizing social relations, revealing inefficiencies, which can then be targeted to develop interventions to enhance communication, foster collaboration, and improve patient safety.

Peer Review reports

Introduction

Adverse events, including medical errors, diagnostic errors, and preventable complications, continue to affect millions of patients globally, leading to severe morbidity, mortality, and substantial avoidable healthcare costs [ 1 , 2 ]. Among the many factors contributing to avoidable adverse events, breakdowns in communication have been identified as a leading cause [ 3 , 4 , 5 ]. Lapses in communication during care coordination and patient handoffs can lead to inadequate patient follow-up, delayed care, increased healthcare costs, and provider burnout, leading to an increased risk of adverse events [ 4 , 6 ].

Many studies have highlighted that investigating the underlying causes and consequences of poor communication is necessary to improve the delivery of high-quality care [ 3 , 4 , 6 , 7 ]. However, a large proportion of studies investigating communication in healthcare organizations lack the necessary understanding of social structures (interconnected relationships of social groups e.g., who speaks to who, for what purpose, using what mechanism) and coordination structures (e.g., how information gets transferred or transitioned between people or services) to make meaningful improvements and reduce adverse events [ 8 , 9 ]. For example, the surgical safety checklist (SSC) is a tool meant to enhance patient safety by coordinating care delivery and improving inter-professional communication [ 10 ]. Yet, many studies report conflicting results on the impact of the SSC due to a lack of mutual understanding of communication among team members (e.g., who is responsible for leading a specific checklist pause point) and coordination (e.g., what team members should be present during specific pause points) structures ( 11 , 12 , 13 ). Effective communication among healthcare providers is challenging due to the complex nature of tasks performed and the numerous healthcare providers embedded within hierarchical structures. While the effective use of Process Improvement or Quality Improvement (QI; framework to systematically improve processes and systems in healthcare) interventions rely on understanding the social interactions and relationships within organizations, little attention has been paid to how social networks can be used to improve the effectiveness of communication and coordination in healthcare.

A social network is a set of social entities, actors or nodes (individuals, groups, organizations) connected by similarities, social relations, interactions, or flows (information) [ 14 ]. Analyzing professional communication structures (e.g., observed formal advice-seeking or giving related to work situations) within healthcare organizations’ social networks is important in understanding how best to inform interventions by identifying which network structures promote or inhibit behavior change [ 15 ]. The use of social network analysis (SNA) can provide insight into the social relationships, interactions, and tasks involved within sociotechnical systems. SNA metrics are quantitative measures used to analyze the structure, relationships, and dynamics within social networks through quantifying network behavior [ 16 ]. Network metrics reflect centrality , which refers to a family of measures where each represent different conceptualizations of nodal importance within a network, and cohesion measures, which examine the extent to which nodes within a network are connected [ 14 , 17 ]. These metrics provide an understanding of the structure of social networks through identifying influential nodes, information flow, communities, and cliques [ 18 ]. SNA has been shown to improve professional communication and interprofessional relationships by revealing gaps in communication and identifying influential social entities and communication channels [ 14 , 15 , 19 ]. By indicating which social entities are effective in the flow of communication, organizations can leverage their skills to disseminate important information effectively and foster positive inter-professional relationships [ 19 , 20 ]. Additionally, through identifying gaps in communication between different teams or departments organizations can work to prevent misunderstandings, adverse events, and the duplication of efforts resulting in a more collaborative work environment with stronger interprofessional relationships [ 14 , 21 ]. Through understanding social networks, SNA can be effective in designing, implementing, and evaluating interventions needed to improve professional communication and coordination in healthcare [ 15 , 22 ].

The aim of this review was to characterize the existing literature to assess SNA methods ability to identify, analyze, and improve processes (Process Improvement) related to patient care within healthcare organizations.

The scoping review was conducted using Arksey and O’Malley’s modified six-step framework [ 23 , 24 ]. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) standards were used to guide the reporting of this review [ 25 ]. The PRISMA-ScR checklist is shown in the Appendix.

Information sources and search strategy

In collaboration with a research librarian (JB), relevant studies were identified through a systematic search of the MEDLINE (Ovid), Embase, Psychinfo, AMED (Allied and Complementary Medicine), CINAHL, Cochrane Library and Web of Science databases from inception – 16 October 2022. The database search was supplemented with hand searching of reference lists of included reviews. Grey literature was searched using Google Custom Search Engine strategies to narrow search results and allow for more targeted results [ 26 , 27 ]. Searched websites included the International Network for Social Network Analysis, American Evaluation Association Social Network Analysis Technical Interest Group, and the International Sunbelt Social Networks Conference proceedings archives. The search strategy for the social network analysis concept was adapted from Sabot et al.’s systematic review of Social Network Analysis and healthcare settings [ 22 ]. Truncation search terms were used to search inclusive and key terms for these concepts can be found in the supplemental appendix.

Eligibility criteria

A screening checklist developed by Sabot et al., 2017 was modified to guide the review of this study [ 22 , 28 ]. A “no” response to any of the study inclusion criteria (Appendix) was a reason for exclusion from the scoping review. “Healthcare providers” were classified as physicians, physician’s assistants, nurses, midwives, pharmacists, pharmacy technicians, clinical officers, counselors, allied health professionals, and other individuals involved in professional networks (e.g., administrative support staff, management). “Professional communication” was defined as observed formal professional advice-seeking or giving related to hypothetical or actual work situations or patients [ 22 ]. Healthcare organizations were defined as a building or mobile enclosure in which human medical, dental, psychiatric, nursing, obstetrical, or surgical care is provided. Healthcare organizations can include but are not limited to, hospitals, nursing homes, limited care facilities, medical and dental offices, and ambulatory care centers [ 29 ]. Studies had to report the use of SNA in the design of the study (e.g., social network mapping, evaluation of network properties or structure, or analysis of network actors) [ 22 ]. Additionally, to be included studies were required to use systematic data-guided activities (e.g., aims and measures) to achieve improvement or use an iterative development and testing process (i.e., Lean Management, Six Sigma, Plan-Do-Study-Act (PDSA) cycles, or Root Cause Analysis) [ 30 , 31 ]. Studies where network relations were defined solely by patient sharing were excluded, as this only predicts person-to-person communication in a minority of instances [ 32 ]. Abstracts and conference proceedings were considered if details of their methodology and results were published. No limits were placed on study design, language, or publication period.

Study selection and screening process

Study selection and screening employed an iterative process involving searching the literature, refining the search strategy, and reviewing articles for study inclusion. The titles and abstracts of all identified references were independently examined for inclusion by three reviewers (T.F, M.D, and L.S) using the Covidence software platform for systematic reviews [ 33 ]. Full texts of potentially eligible studies were retrieved by the reviewers (T.F, M.D, and L.S), who determined study eligibility using a standardized inclusion screening checklist. Inter-rater reliability was assessed at each phase of the scoping review between reviewers and disagreements were resolved by consensus with input from a fourth author (L.J).

Charting the data

Data from eligible full-text studies was charted by the reviewers (T.F, M.D, L.S) independently using a standardized data abstraction form in Covidence to obtain key items of information from the primary research reports. Discrepancies among reviewers were resolved by consensus. The data abstraction form captured information on key study characteristics (e.g., author, year of publication, location of study, study design, aim of study, type of healthcare facility/provider), SNA-related information (e.g., SNA purpose, data collection methodology, software, SNA metrics) and reported on the implications of using SNA (e.g., social network mapping, assessment of network members or structures).

Collating, summarizing, and reporting the results

A narrative synthesis was performed to describe the study characteristics, SNA methodology, and SNA metrics. The stages of the narrative synthesis included: (1) developing the preliminary synthesis, (2) comparing themes within and between studies, and (3) thematic classification [ 34 ]. Detailed text data on SNA characteristics and implications were reviewed, re-categorized, and analyzed thematically. In line with our objectives, the thematic analysis focused on identifying SNA methods used to improve communication and coordination in healthcare organizations. To categorize the approaches, we conducted further distillation of overarching approaches. We took notes throughout the review and analysis stages, documenting emerging trends and ideas to facilitate further review and discussion among the review team. The extracted data was tabulated in descriptive formats and narrative summaries were provided.

The literature search generated 5084 potentially eligible studies after deduplication, of which 4936 were excluded based on title and abstract, leaving 148 full-text articles to be reviewed. The PRISMA-ScR flow diagram outlining the breakdown of studies can be found in Fig.  1 . Upon full-text review, 44 reports of 38 studies were included for data abstraction. Six studies [ 4 , 35 , 36 , 37 , 38 , 39 ] had multiple records and were truncated into single studies.

PRISMA-ScR flow diagram

Study characteristics

The characteristics of the included studies are shown in Table  1 . Many studies were recently published between 2015 and 2021 (26, 68%) and were primarily located in the United States (26, 68%). 67% of studies occurred within a hospital (25, 66%) and most studies (15, 39%) were set in Internal medicine (gastroenterology, oncology, cardiology, nephrology, respirology, telemetry, or acute care). Studies employed multidisciplinary healthcare providers, however many studies focused on physicians (endocrinologists, oncologists, plastic surgeons, neurologists, anesthesiologists, intensivists, generalists; 27, 71%) and nursing staff (registered nurse, nurse practitioner, practical nurse; nursing assistants; 27, 71%). Most studies employed an observational study design, with 5 studies utilizing longitudinal quasi-experimental design [ 40 , 41 , 42 , 43 , 44 ]. Five studies used mixed-methods designs [ 35 , 36 , 45 , 46 , 47 ] with integrated qualitative and quantitative data, and a further 6 studies used multi-method designs [ 48 , 49 , 50 , 51 , 52 , 53 ] using a combination of independent qualitative and quantitative data. Twenty-four studies reported using quantitative data only [ 3 , 4 , 6 , 40 , 41 , 42 , 43 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 ] and the remaining 2 studies used qualitative methods [ 71 , 72 ].

Table  2 provides an overview of the aims and findings of the included studies and Table  3 outlines the use of SNA methodology and reflects the data collection methods, software, and SNA metrics included in each study. A wide range of network visualization software was used with studies giving preferences towards UCINET [ 36 , 40 , 48 , 54 , 57 , 58 , 59 , 66 , 67 , 68 , 70 , 72 , 73 ], Organization Risk Analyzer (ORA) [ 4 , 55 , 74 , 75 ], and Open-Sourced R Software [ 42 , 49 , 53 , 63 , 65 , 76 ]. Five out of the 38 studies did not visualize their networks through social network mapping and only provided a descriptive assessment of network structures or analysis of network members [ 3 , 40 , 57 , 68 , 76 ]. Two studies did not explicitly report SNA metrics [ 47 , 61 ]. Table  4 provides a comprehensive breakdown of the SNA metrics selected in each study and their application to healthcare networks. There were many network metrics used throughout the studies, however, most studies primarily employed Degree Centrality, Betweenness Centrality, and Density. Twenty-six studies used Degree Centrality as a measure of reach and importance [ 3 , 4 , 6 , 35 , 36 , 41 , 43 , 44 , 45 , 46 , 48 , 49 , 51 , 54 , 55 , 56 , 57 , 58 , 59 , 62 , 63 , 64 , 65 , 67 , 69 , 70 ], 20 studies used Density to measure network cohesion [ 6 , 35 , 36 , 41 , 43 , 44 , 45 , 48 , 53 , 54 , 55 , 57 , 58 , 62 , 63 , 69 , 70 , 71 , 72 , 77 ], and 19 studies used Betweenness Centrality as a measure of influence and brokerage [ 3 , 4 , 36 , 44 , 45 , 46 , 49 , 51 , 52 , 55 , 56 , 57 , 59 , 60 , 62 , 63 , 65 , 66 , 69 ].

* Some articles were assigned to more than one category.

Listed in descending frequency, however “Other” is always at the bottom.

Application and findings of SNA

SNA has been used in healthcare to measure the number of connections (i.e., interactions, tasks), the centrality of providers (i.e., degree, betweenness, and closeness), and network cohesion (i.e., density, clustering). It has helped us to understand essential themes like organizational structure, team performance, and influential actors in healthcare.

a) Organizational Structure.

SNA has been used to better understand how organizational structures (e.g., management roles, groupings of tasks and employees) influence communication and coordination, thereby informing opportunities for improvement. Nine studies showed how SNA was used to redesign hospital organizational structures [ 35 , 36 , 41 , 45 , 46 , 53 , 66 , 69 , 72 ]. For example, Samarth et al. [ 69 ] applied SNA to improve the throughput of their surgical patients, which revealed a hierarchical network coordination structure in their post-anesthesia care unit (PACU) wherein the Charge Nurse channeled all communication downstream, thereby becoming a bottleneck resulting in patient delays. This led to a redesign of their organizational network to a more democratic structure where coordination was performed by an integrated information technology (IT) system which was available to all team members, reducing the dependence on the charge nurse [ 69 ]. Additionally, Alhaider et al. [ 52 ] demonstrated how SNA could be used to investigate system-wide communication in patient flow management and identify process improvement within the healthcare system. Applying SNA within the Distributed Situation Awareness (DSA) framework helped identify bottlenecks in patient flow and the roles that were most likely to experience communication or transaction overload while acquiring and disseminating situational awareness. The DSA model provided a characterization of patient flow and a blueprint for healthcare facilities to consider when modifying their organizational structure to improve communication and coordination. Spitzer-Shohat et al. [ 36 ] used SNA to understand how their organizational structure could help implement disparity reduction interventions to improve care. The SNA unveiled that their subregional management had a high degree of centrality (i.e., many connections), and as such, they were targeted to spread information about the interventions [ 36 ].

A specialized application of SNA involves identifying how IT can enhance or transform organizational communication and coordination. Three studies used SNA to understand how providers from different professions and units communicate across various modes (e.g., in-person, phone, electronic medical record) [ 4 , 48 , 69 ]. For example, SNA highlighted that IT could help improve communication efficiencies during in-person patient handoffs. More specifically, SNA showed that IT could support the redesign of the social network patterns by removing redundant communication exchanges and support emergent and non-linear information flow [ 4 , 69 ]. Six studies used electronic health records (EHR) data to map the network structure of professionals involved in care to show that improving the design of IT can support communication leading to more frequent information sharing among professional groups [ 6 , 47 , 51 , 56 , 60 , 63 ]. Nengliang et al. [ 56 ] demonstrated that EHR log data could be used within an SNA to map the network structure of all healthcare providers and examine the connectivity, centrality, and clustering of networks that emerged from interactions between providers who shared patients. In turn, this data revealed the dynamic nature of care teams and areas (inpatient and outpatient) for collaborative improvement [ 56 ]. Another study used SNA to help contrast low and high IT implementations; they found that the high IT sophistication care homes had more robust and integrated communication strategies requiring fewer face-to-face interactions between providers to verify orders or report patient status compared to the low IT sophistication nursing home [ 47 ].

b) Team Performance.

Sixteen studies used SNA to examine poor team communication and coordination by highlighting the inefficiencies in health networks [ 3 , 36 , 41 , 43 , 53 , 54 , 55 , 57 , 58 , 61 , 64 , 65 , 67 , 68 , 70 , 71 ]. SNA identified that these inefficiencies stem from: teams being overburdened due to workload [ 54 , 61 ], conflict between team roles [ 36 ], lack of leadership [ 43 , 58 ], and fragmented interprofessional relationships [ 57 , 65 , 70 ]. For example, poor team performance in hospital emergency departments has resulted in congestion and increased length of stay with patients having prolonged discharges. SNA allowed for an exploration of the possible causes of inefficiencies resulting in access blocks and determined that the number of healthcare providers and interactions between them, and the centralization of providers within the network affected the performance and quality of emergency departments [ 54 ]. Grippa et al. [ 3 ] used SNA and determined that the most efficient and effective healthcare teams focused more inwardly (internal team operation) and were less connected to external members. Additionally, SNA highlighted that effective teams communicated using only one or two mediums (e.g., in-person, email, instant messaging media) instead of dispersing time on multiple media applications.

SNA has been used to diagnose possible reasons for team inefficiencies and to identify potential design solutions to improve team performance [ 3 , 35 , 42 , 53 , 64 , 67 , 68 , 71 ]. A study used SNA to identify that some experienced staff (who frequently mentor other staff) may have too many connections (high degree of centrality), leading to interruptions or distractions and impacting performance and coordination [ 54 ]. However, a different study, identified that staff with a high degree of centrality have the benefit of improving team performance by leveraging their social networks to be change agents and lead others to replicate desired behaviors (e.g., when a provider may forget to implement a desired change but gets reminded by a team member) [ 62 ]. Lastly, analyzing network cohesion helped identify fragmentation and cliques in the network which may reflect a lack of collaboration and interprofessional relations. For instance, denser (more connections) communication networks with more clustering (groups of connections) are associated with more rapid diffusion of information. Additionally, the connections between providers in dense networks can provide social support (reinforcement) to team members that strengthen their commitment to follow desired behaviors and increase the likelihood that deviations from those actions will be noted by their peers [ 62 ].

c) Influential Actors.

SNA was used to identify influential actors who could act as brokers (an individual who occupies a specific structural position in systems of exchange) [ 3 , 49 , 64 ] who could become opinion leaders (an individual who holds significant influence over others’ attitudes/beliefs) [ 62 ], champions (an individual who actively supports innovation and its promotion/implementation) [ 40 ] or a change agent (an early adopter of an intervention who supports the dissemination of its use) [ 44 ] based off measures of social influence within a network. Studies showed that influential actors in social networks can inform behavioral interventions needed to improve professional communication or coordination [ 3 , 40 , 49 , 62 , 64 ]. For example, Meltzer et al. [ 62 ] used SNA to identify influential physicians to join a QI team and highlighted that having members with connections external to the team is most important when disseminating information, while within team relationships matter most when coordination, knowledge sharing, and within-group communication are most important. When creating an interdisciplinary team, betweenness centrality (node that frequently lies on the shortest path in a network) may be a useful network metric for prospectively identifying team members that may help to facilitate coordination within and across units / professional groups. Providers with a high betweenness have been found to be leaders and active participants in task-related groups [ 68 ]. Hurtado et al. [ 40 ] used SNA to identify and recruit champions who were used to deploy a QI intervention (safe patient handling education program) to advance safety in critical access hospitals. The champion-centered approach resulted in improved safety outcomes (increase in safety participation/compliance and decrease in patient-assist injuries) after one year. Additionally, Lee et al. [ 44 ] used SNA to assess the use of peer-identified and management-selected change agents on improving hand hygiene behavior in acute healthcare. No significant differences were reported between the two groups; however providers expressed a preference for hierarchical leadership styles highlighting the need to understand organizational culture before designing changes to the system.

This scoping review presents a comprehensive overview of the existing literature looking at the use and impact of SNA methodology on Process Improvement within healthcare organizations. Our search strategy included a wide range of databases and placed no restrictions on study design, language, or publication period. When examining the expanding body of literature represented in our identified 38 studies, SNA methods were used to detect essential work processes in organizations, reveal bottlenecks in workflow, offer insight into resource allocation, evaluate team performance, identify influential providers, and monitor the effectiveness of process improvements over time. By analyzing the communication and relationships between management roles, employee groupings, and task allocation, SNA provides insights that can help identify areas for improvement related to patient throughput, diffusion of information, and the uptake of technology (e.g., IT systems). Studies highlighted that healthcare team performance can be hampered by inefficiencies related to being overburdened due to workload, conflicts between team roles, lack of leadership, and fragmented interprofessional relationships. To address these inefficiencies, SNA can leverage network outcomes related to connectedness (e.g., degree, betweenness, closeness) and use knowledge of the network structure (e.g., density, clustering coefficient, fragmentation) to create targeted interventions to mitigate these problems. Additionally, inefficiencies in social networks can be mitigated by identifying influential actors who serve as change agents and can be utilized as opinion leaders or champions to improve the efficiency of information exchange and the uptake of behavioral interventions.

Comparison With Past Literature (Study Design and Data Collection).

Our review stands out from previous studies due to its unique focus on the application of SNA methods in Process Improvement within healthcare organizations. Our primary objective was to investigate how healthcare organizations utilize SNA techniques to improve system-level coordination and enhance the overall quality of care provided to patients. In their research study, Sabot et al. [ 22 ] aimed to investigate the various SNA methods employed to examine professional communication and performance among healthcare professionals. Their study delved into the diverse range of SNA techniques used to gain insights into the complex network dynamics and interactions among providers. In more recent studies, Saatchi et al. [ 78 ] focused on exploring the adoption and implementation of network interventions in healthcare settings. This study provided insights into the effectiveness of network interventions (in which contexts they are successful and for whom), their potential benefits (increased volume of communication), and the challenges associated with their adoption in practice. Additionally, Rostami et al. [ 79 ] focused on advancing quantitative SNA techniques and investigated the application of community detection algorithms in healthcare. This study offers a comprehensive categorization of SNA community detection algorithms and explores potential approaches to overcome gaps and challenges in their use. Previous reviews primarily included observational and cross-sectional study designs with no comparator arms, which made determining the value of using SNA methods difficult as there was no comparison of social networks over time and no comparable head-to-head data. Our review identified 5 quasi-experimental studies [ 40 , 41 , 42 , 43 , 44 ] which used longitudinal or pre-post study designs. In each of these studies SNA was used to review a system which delivered clinical care to identify sources of variation and areas for process improvement at an individual and organizational level. The quasi-experimental studies were published within the last 5 years, indicating that SNA methodology is still in development and opportunities for experimental and longitudinal study designs are forthcoming. Using experimental and longitudinal SNA methods would enable causal inference of healthcare interventions or policies leading to improved generalizability of results.

When performing SNA there is a variety of qualitative (interviews, focus groups, observations) and quantitative (surveys, document artifacts, information systems) methods that researchers can use to map social networks, assess network structures, and analyze team actors. However, previous literature reviews have outlined an overreliance on descriptive SNA methods, which lack the contextual factors needed to interpret how a network reached a given structure. There has been a growing body of evidence advocating for the use of mixed-method social network data collection [ 80 ]. Our review has highlighted an increased uptake of mixed-method (integration of qualitative and quantitative methods and data) and multi-method (independent use of quantitative and qualitative methods) SNA study designs [ 81 ].

Knowledge Gaps and Future Research.

This scoping review highlights many practical uses of SNA; however, within most studies, little attention has been paid to leveraging SNA theory to help explain why networks have the structures they do [ 21 ]. For example, social boundaries between professional groups (e.g., Physicians, Nurses, Pharmacists) can inhibit the development of interprofessional networks though the creation of cliques leading to strong communication and coordination within groups, but fragmented communication across professional groups [ 21 , 82 , 83 ]. A potential explanation for the scarcity of studies assessing the reasons behind the structures of networks could be attributed to the primarily quantitative SNA methods used. Few studies used a qualitative or mixed-method design, indicating a limited understanding of the contextual factors associated with social networks. SNA can reveal the informal structures within organizations and underscores the importance of understanding that not all influential relationships between healthcare providers are found on formal organizational charts, and that informal networks can significantly influence communication and coordination [ 84 ]. The lack of robust study designs (mixed-method or multi-method) may also reflect the use of SNA by researchers more so as a technique than a methodology with theoretical underpinnings.

The value of using SNA to inform research and disseminate evidence-based interventions and policies has been discussed in the literature extensively. However, very few studies have used research on complex systems and network theory to examine how HCWs can act as change agents, interacting within and between hubs in organizations to disseminate knowledge [ 85 ]. Future research should apply complexity science to SNA to reconceptualize knowledge translation and think of the process as interdependent and relationship-centric to support sustainable translation [ 85 ]. Only a small group of included articles have highlighted how leveraging influential actors as change agents such as opinion leaders or champions can be advantageous in improving professional communication or coordination [ 3 , 40 , 44 , 49 , 62 , 64 ]. This review identified two studies [ 40 , 44 ] which utilized SNA and a champion-centered approach to support the successful implementation of a QI intervention resulting in improved safety outcomes. The use of champions is very prevalent in healthcare; however, success rates vary widely, likely due to the poor selection of champion candidates or organizational culture [ 40 , 44 ]. In many cases healthcare workers selected to be champions are volunteered and do not hold enough social influence to change the behaviors of their colleagues. In the future SNA methods should be used to identify influential champions or opinion leaders embedded within their social networks who can influence knowledge transfer and facilitate coordination leading to process improvements.

Future research should identify how SNA methods can leverage health informatics and the large amounts of data stored within healthcare organizations. Even though past studies have used SNA to enhance organizational communication and coordination using IT [ 47 , 56 , 69 ], applying SNA to artificial intelligence and machine learning (ML) algorithms has not received much attention [ 86 ]. Integrating ML algorithms into community detection techniques has showcased the diverse ways SNA can be utilized in healthcare to monitor disease diagnosis, track outbreaks, and analyze HCW networks [ 79 ].

Limitations of the Review.

This review has some limitations that should be acknowledged. First, we excluded studies of provider friendship networks, which theoretically may have contained some professional communication. Secondly, we excluded studies where network relations were defined solely by patient sharing, as this has only been shown to predict person-to-person communication in a minority of instances. Lastly, studies were required to incorporate a Process Improvement component. Different terms were used to describe Process Improvement in the literature, making it challenging to devise a search strategy that would yield sufficient articles for review while also utilizing SNA methods. As a result, studies that utilized SNA methods but did not explicitly examine a process or system for delivering clinical care to identify sources of variation and areas for improvement were excluded.

SNA methods can be used to characterize Process Improvements through mapping, quantifying, and visualizing social relations revealing inefficiencies, which can then be targeted to develop interventions to enhance communication, foster collaboration, and improve patient safety. However, healthcare organizations still lack an understanding of the benefit of using SNA methods to reduce adverse events due to a lack of experimental studies. By emphasizing the importance of understanding professional communication and coordination within healthcare teams, units, and organizations, our review underscores the relationship between organizational structures and the potential of influential actors and emerging IT technologies to mitigate adverse events and improve patient safety.

Data availability

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

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Acknowledgements

The authors would like to thank Joanna Bielecki for her assistance in developing the search strategy and Sonia Pinkney for her valuable feedback and suggestions in refining this manuscript.

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Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada

Troy Francis, Morgan Davidson, Laura Senese, Lianne Jeffs, Valeria Rac & Patricia Trbovich

HumanEra, Research and Innovation, North York General Hospital, Toronto, ON, Canada

Troy Francis & Patricia Trbovich

Program for Health System and Technology Evaluation, Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada

Troy Francis & Valeria Rac

Department of Public Health Sciences, University of Rochester, New York, USA

Reza Yousefi-Nooraie

Department of Political Science, Université Laval, Quebec, Canada

Mathieu Ouimet

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Francis, T., Davidson, M., Senese, L. et al. Exploring the use of social network analysis methods in process improvement within healthcare organizations: a scoping review. BMC Health Serv Res 24 , 1030 (2024). https://doi.org/10.1186/s12913-024-11475-1

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Non-linear associations between night shifts and adverse events in nursing staff: a restricted cubic spline analysis

• Mao Xiaolan 1   na1 ,
• Zhizhou Duan 2   na1 ,
• Zhiping Niu 3 ,
• Jianmei Jiang 1 ,
• Xiang Wei 1 &
• Xiangfan Chen 4  

BMC Nursing volume  23 , Article number:  602 ( 2024 ) Cite this article

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Introduction

Existing studies suggest that the number of night shifts may impact the occurrence of adverse events. However, while this relationship is well-documented, previous research has not thoroughly examined the non-linear associations between night shifts and adverse events among nursing staff, which remains a gap in our understanding.

Participants were 1,774 Chinese nurse staff. Psychosocial characteristics were screened by The Chinese version of the multidimensional scale of perceived social support (MSPSS) for social support, the 9-item Patient Health Questionnaire (PHQ-9) for depressive symptoms, the Generalized Anxiety Disorder-7 (GAD-7) for anxiety symptoms. Binary logistic regression and restricted cubic splines were applied to analyze the data. The statistical software used were R version 3.6.2 and SPSS version 22.0.

Over the past year, 325 cases (18.3%) were classified as adverse events. Logistic regression unveiled that social support played a protective role against adverse events, with an OR of 0.991 (95% CI: 0.983, 0.999). Furthermore, night shifts continued to surface as a substantial risk factor for adverse events, with an OR to 1.300 (95% CI: 1.181, 1.431). The restricted cubic spline regression model highlights a nonlinear relationship between night shifts and adverse events (P for non-liner < 0.001). The probability of adverse events increases with the number of night shifts, but compared to individuals working 3–4 night shifts per month, those working 5–6 night shifts per month have a lower probability of adverse events.

Our findings indicate a non-linear relationship between the frequency of night shifts and adverse events, suggesting a complex interplay of factors. This highlights the need for nursing practice and policy to consider the intricacies of night shift scheduling and explore more reasonable rostering strategies to mitigate the probability of adverse events.

Peer Review reports

Adverse events, defined as unintended injuries or complications caused by medical management rather than the patient’s underlying disease process, can lead to prolonged hospital stays, disabilities, or even death [ 1 ]. Numerous studies have reported higher rates of adverse events worldwide. For instance, Kakemam conducted a cross-sectional online study among 1,004 Iranian nurses and reported rates of adverse events ranging from 26.1 to 71.7% [ 2 ]. In Canada, adverse events occur in an estimated 7.5% of all hospitalizations [ 1 ], while Gaita´n-Duarte et al. found a 4.6% incidence of adverse events in their study conducted in Colombia [ 3 ]. It is noteworthy that an analysis of 25 studies across 27 countries revealed that 10.0% of all in-patients experienced an adverse event, with over 80.0% of these events being deemed preventable [ 4 ]. While adverse events are inevitable due to human imperfection [ 5 ], controlling nursing-related factors is essential for safeguarding patient safety [ 6 ].

Shift work is a common aspect of hospital nursing, as nurses are required to provide around-the-clock healthcare. However, working overnight shifts can lead to adverse health effects. Past research has shown that night shift work can disrupt circadian rhythms, disturb sleep, and induce various behavioral changes [ 7 ]. These effects may elevate the risk of chronic diseases [ 8 , 9 , 10 ], mental disorders [ 11 , 12 , 13 ], cognitive impairment [ 14 , 15 ], and even mortality [ 14 , 16 ]. These problems not only affect nurses’ work efficiency but may also lead to adverse events such as medication errors, patient falls, and delays in care, seriously threatening patient safety and the quality of nursing. In Abdalkarem F’s study, nurses working in public hospitals across various regions of Saudi Arabia were surveyed. The study included 1,256 participants and revealed that the majority of respondents (85.7%) experienced patient safety and performance issues attributed to night shift rotations [ 17 ]. Additionally, a significant number of respondents (93.6%) reported experiencing physiological effects as a result of these night shifts [ 17 ]. Although hospitals around the world are striving to reduce the frequency of nurses’ night shifts, in reality, a large number of nurses still maintain a high frequency of night shifts. Therefore, we must pay close attention to the impact of nurses’ night shift frequency on the incidence of adverse events.

Existing studies suggest that the number of night shifts may significantly impact the occurrence of adverse events. Niu et al. (2013) conducted a randomized study involving 62 nurses, which revealed that the error rate on a standardized test for those working night shifts was 44% greater than that of nurses on fixed day shifts [ 18 ]. Similarly, Johnson et al. (2014) discovered that among 289 nurses working night shifts, 56% experienced sleep deprivation, and those who were sleep-deprived made a higher average number of patient care errors compared to their non-sleep-deprived counterparts [ 19 ]. In Lina’s study, a notable variance in adverse events was observed between night shifts and day shifts [ 20 ]. A recent meta-analysis also indicated that the frequency of night shifts contributes to an increased likelihood of errors, with an error rate recorded at 20.5% [ 21 ]. However, there is still a lack of evidence to thoroughly examine the relationship between night shifts and adverse events in China. Furthermore, the current research methods primarily focus on linear or logistic regression analysis, which may not fully capture the complex association between the number of night shifts and the incidence of adverse events.

Additionally, night shiftwork may also have physiological effects on nurses. Some studies have reported that adverse effects of night shiftwork on the physiological status of nurses include anxiety, depression, and social support [ 22 , 23 , 24 ]. Furthermore, a study reported that 36% of health workers indicated that night work had an impact on their fatigue levels [ 25 ]. These psychological conditions may influence the occurrence of adverse events [ 26 , 27 , 28 ]. Therefore, it is essential to control for these psychosocial factors.

To our knowledge, the number of night shifts worked by nurses is significantly correlated with their sleep quality. The frequency of night shifts can greatly affect nurses’ sleep duration, and maintaining an optimal amount of sleep—guided by a balanced schedule of night shifts—is essential for their overall health and performance [ 29 , 30 ]. Deviations from this ideal sleep duration, whether too excessive or too limited, can disrupt physiological processes and cognitive functions [ 31 , 32 ], thereby increasing the likelihood of adverse events [ 33 ]. Consequently, both an excessive and insufficient number of night shifts may contribute to a higher incidence of adverse events among nurses. This leads us to hypothesize that there exists a non-linear relationship between an appropriate number of night shifts and the occurrence of adverse events. Therefore, this study aimed to uncover both linear and nonlinear associations between night shifts and adverse events. Through this, we can optimize the work arrangement of nurses, reduce the incidence of adverse events, and further improve the quality and safety of nursing services.

Participants and procedures

A comprehensive cross-sectional survey was conducted among nurses employed in 18 public hospitals in Dehong City, Yunnan Province. Participants were selected using a convenient sampling method and completed the questionnaires via Wenjuanxing, an online survey platform widely used in China. The nursing departments of each government hospital played a crucial role in distributing the questionnaire link while ensuring participant anonymity and independence during completion. To be included in the study, participants had to meet specific criteria, including current employment at one of the 18 local government hospitals, comprehension of the questionnaire content, willingness to participate with informed consent, and no history of diagnosed mental illness or student nurse status. Participants were informed of their right to withdraw at any point, and it typically took around seven minutes to complete the questionnaire.

In total, 1,965 caregivers were invited, and we achieved a response rate of 90.3% ( n  = 1774). This research was conducted in accordance with ethical principles and was approved by the Ethics Committee of Dehong people’s hospital (Approval No. DYLL-KY032). Prior to participation, all caregivers were provided with detailed information about the study, including its purpose, procedures, potential risks and benefits, and their rights as participants. They were also informed that their participation was voluntary and that they had the right to withdraw from the study at any time without any negative consequences. Informed consent was obtained from all participants before they completed the questionnaire.

Socio-demographic variables

The participants’ basic socio-demographic characteristics included age, gender, ethnicity, marital status, place of residence, level of education, whether they were an only child, and monthly income.

Night shift

In this study, we implemented a specific item to assess the frequency of night shifts by asking participants, “In the past years, how many night shifts do you work per month?” The response options include various ranges of night shift frequencies: 0 shifts, 1–2 shifts, 3–4 shifts, 5–6 shifts, and 7 shifts or more [ 8 ]. Respondents are required to choose the option that most accurately reflects their individual circumstances.

Depressive symptoms

Depressive symptoms among the participants were assessed using the 9-item Patient Health Questionnaire (PHQ-9) [ 34 ], which utilizes a 4-point Likert scale with responses ranging from “not at all” to “nearly every day,” corresponding to scores from 0 to 3. The total score on the PHQ-9 can range from 0 to 27, with higher scores indicating more severe depressive symptoms. A commonly utilized threshold of 10 is typically applied to differentiate between depressive symptoms and the absence of such symptoms [ 35 ]. The Chinese version of the PHQ-9 has been shown to have good validity and reliability in the Chinese context [ 36 , 37 ]. In this study, the Cronbach’s alpha coefficient for the PHQ-9 was calculated as 0.91. The correlation coefficient between the total score and each item ranged from 0.352 to 0.820, with a significance level of P  < 0.001. The KMO score was 0.928, and the Bartlett’s test of sphericity produced an approximate χ2 value of 8817.595, with P  < 0.001, showing strong reliability and validity.

Anxiety symptoms

Anxiety symptoms among the participants were evaluated using the Generalized Anxiety Disorder-7 (GAD-7) scale [ 38 ]. This assessment tool comprises 7 items and utilizes a 4-point response scale, with responses ranging from 0 (representing “not at all”) to 3 (indicating “nearly every day”). The total score on the GAD-7 can vary from 0 to 21, with higher scores indicating more pronounced anxiety symptoms. A threshold score of 10 is generally employed to differentiate between anxiety symptoms and the absence of anxiety [ 39 ]. The Chinese version of the GAD-7 has demonstrated strong validity and reliability in the Chinese context [ 36 , 40 ]. In the present study, the Cronbach’s alpha coefficient for the GAD-7 was calculated as 0.93. The correlation coefficient between the total score and each item ranged from 0.540 to 0.871, with a significance level of P  < 0.001. The KMO score was 0.927, and the Bartlett’s test of sphericity produced an approximate χ2 value of 8581.425, with P  < 0.001, showing strong reliability and validity.

Social support

Social support was assessed by the multidimensional scale of perceived social support (MSPSS) [ 41 ], initially developed by Zimet and subsequently translated into Chinese by Jiang Qianjin, serves as a tool for assessing an individual’s multidimensional social support, encompassing family support (items 3, 4, 8, 11), friend support (items 6, 7, 9, 12), and other forms of support (items 1, 2, 5, 10). The Chinese version of this scale have been used in Chinese population [ 42 , 43 ]. And the Cronbach’s α = 0.96 in this study. The correlation coefficient between the total score and each item ranged from 0.582 to 0.907, with a significance level of P  < 0.001. The KMO score was 0.941, and the Bartlett’s test of sphericity produced an approximate χ2 value of 23322.441, with P  < 0.001, showing strong reliability and validity.

Level of fatigue

In this study, we employed a single-item question: “What number best represents your current level of fatigue?” [ 44 ] Participants were requested to choose a number from 0 to 10 that best reflected their current feelings regarding their level of fatigue. This scale encompasses a range from “No Fatigue” to “Extreme Fatigue,” with a total of 11 levels. For example, selecting “0” signifies that the participant experiences no fatigue, whereas choosing “10” indicates an extreme level of fatigue.

• Adverse events

In this study, we employed a single-item question: “Have you experienced any nursing adverse events in the past year?“ [ 2 ]. Additionally, we provided a specific definition for “nursing adverse events,” which includes situations such as ‘Pressure ulcers’, ‘Patient falls’, ‘Medication errors’, ‘Surgical wound infections’, ‘Infusion or transfusion reactions’, ‘Patient and family verbal abuse’, and ‘Patient or family complaints.’ These events encompass various circumstances resulting from nurses’ lack of accountability, failure to adhere to operational protocols, or lapses in implementing essential systems. Participants were required to select either “Yes” or “No” based on this defined criteria to indicate if they had encountered such events within the past year.

Statistical analysis

In our study, we conducted a descriptive analysis to gain insights into the data. Qualitative variables were presented using frequencies and percentages (N/%), while quantitative data were described using means ± standard deviations (SDs).

To further explore the relationship between night shifts and adverse events, logistic regression models were utilized. Three logistic models were constructed to investigate this association: Model 1 examined adverse events in relation to night shifts; Model 2 included basic socio-demographic covariates along with night shifts; and Model 3 incorporated basic socio-demographic factors, psychosocial variables, and night shifts.

Furthermore, a dose-response analysis was carried out to investigate the non-linear correlation between night shifts and adverse events. This analysis employed restricted cubic splines (RCS), a flexible modeling technique that captures intricate non-linear relationships. RCS can be viewed as a piecewise polynomial regression method, fitting data with cubic polynomials between specified knots while preserving linearity or constancy outside of these knots. This approach effectively mitigates the risk of overfitting. The data were adjusted for multiple covariates such as age, sex, ethnicity, marital status, residence, education level, only-child status [ 45 ], income [ 46 , 47 ], depressive symptoms, anxiety symptoms, social support, and level of fatigue. The RCS was fitted to the data with knots positioned at the 5th, 35th, 65th, and 95th percentiles of night shift duration, with the 5th percentile serving as the reference point. Wald tests were conducted to evaluate the statistical significance of the non-linear trends identified by the RCS coefficients.

It is important to highlight that the RCS analysis was performed using R version 3.6.2, along with the “rms” and “ggplot2” packages, while all other statistical analyses were conducted using SPSS version 22.0. For all statistical tests in this study, a significance level of 0.05 was set, and a two-tailed approach was adopted for hypothesis testing.

Out of the total participant pool of 1,774 nurses in the statistical analysis, 325 cases (18.3%) were identified as adverse events. Table  1 outlines the baseline socio-demographic characteristics and psychological outcomes of the participants. The majority of the participants were female, comprising 1,666 individuals (93.9%), while 498 participants (28.1%) were classified as ethnic minorities. The average age of the participants was 32.00 ± 7.99, with a notable portion (674 or 38.0%) falling within the 25–29 age range. Marital status data indicated that 1,200 individuals (67.6%) were married, with 1,071 participants (60.4%) living in rural areas. Educational background revealed that around one-third of nurses (614 or 34.6%) had completed education up to high school level or lower. In terms of income, the majority reported earnings between 3001 and 7000, with 1,107 participants (62.4%) falling into this category. The mean values for various psychological parameters and occupational factors were as follows (Table  2 ): depressive symptoms (7.42 ± 5.13), anxiety symptoms (6.29 ± 4.32), social support (62.60 ± 14.02), fatigue levels (5.55 ± 2.50), and night shifts (2.95 ± 1.46).

In Table  3 , logistic regression analysis was performed to explore the relationship between night shifts and adverse events. Model 1 includes only the night shift as a variable, Model 2 adds general demographic characteristics alongside the night shift, and Model 3 further incorporates psychological factors in addition to the general demographic characteristics and the night shift. In model 1, night shifts emerged as a notable risk factor for adverse events, exhibiting an odds ratio (OR) of 1.336 (95% CI: 1.223, 1.459). Transitioning to model 2, the analysis revealed being an only child as a risk factor for adverse events, with an OR of 1.426 (95% CI: 1.002, 2.016). Despite a slight reduction, night shifts retained significance as a risk factor for adverse events in this model, with an OR of 1.330 (95% CI: 1.211, 1.461). Notably, no other variables demonstrated statistically significant associations with adverse events within this model. Advancing to model 3, the investigation unveiled that social support played a protective role against adverse events, with an OR of 0.991 (95% CI: 0.983, 0.999). Furthermore, night shifts continued to surface as a substantial risk factor for adverse events, showcasing a notable shift in the OR to 1.300 (95% CI: 1.181, 1.431). Amidst these findings, no other variables exhibited statistically significant links with adverse events.

The restricted cubic spline regression model, depicted in Fig.  1 , highlights a nonlinear relationship between night shifts and adverse events. When considering various factors such as age, sex, ethnicity, marital status, residence, education level, being an only child, income, depressive symptoms, anxiety symptoms, social support, and level of fatigue, it becomes evident that the likelihood of adverse events escalates with an increase in monthly night shifts. Intriguingly, the probability of adverse events decreases for individuals working 5–6 night shifts per month compared to those working 3–4 night shifts, before exhibiting a rise with further increments in monthly shifts. Subsequent RCS analyses conducted for distinct genders (Fig.  2 a) and age groups (Fig.  3 b) unveil consistent patterns akin to Fig.  1 . Moreover, it was noted that, given an equivalent number of night shifts, women manifested a lower probability of adverse events compared to men. Among diverse age brackets, nurses aged 30–34 exhibited the highest probability of encountering adverse events.

Non-liner association between night shift and adverse events using a Restricted Cubic Spline Regression Model. Graphs show ORs for adverse events according to night shift adjusted for age, sex, ethnic, marital status, residence, education level, only child, income, depressive symptoms, anxiety symptoms, social support, level of fatigue. Data were fitted by a logistic regression model, and the model was conducted with 4 knots at the 5th, 35th, 65th, 95th percentiles of nightshift (reference is the 5th percentile). Solid lines indicate ORs, and shadow shape indicate 95% CIs. OR, odds ratio; CI, confidence interval

Non-liner association between night shift and adverse events using a Restricted Cubic Spline Regression Model, separated by sex. Graphs show ORs for adverse events according to night shift adjusted for age, sex, ethnic, marital status, residence, education level, only child, income, depressive symptoms, anxiety symptoms, social support, level of fatigue. Data were fitted by a logistic regression model, and the model was conducted with 4 knots at the 5th, 35th, 65th, 95th percentiles of nightshift (reference is the 5th percentile). Solid lines indicate ORs, and shadow shape indicate 95% CIs. OR, odds ratio; CI, confidence interval

Non-liner association between night shift and adverse events using a Restricted Cubic Spline Regression Model, separated by age. Graphs show ORs for adverse events according to night shift adjusted for age, sex, ethnic, marital status, residence, education level, only child, income, depressive symptoms, anxiety symptoms, social support, level of fatigue. Data were fitted by a logistic regression model, and the model was conducted with 4 knots at the 5th, 35th, 65th, 95th percentiles of nightshift (reference is the 5th percentile). Solid lines indicate ORs, and shadow shape indicate 95% CIs. OR, odds ratio; CI, confidence interval

In this study, we identified social support as a protective factor against adverse events, with an odds ratio (OR) of 0.991 (95% CI: 0.983, 0.999). Additionally, night shifts emerged as a significant risk factor for adverse events, with an OR of 1.300 (95% CI: 1.181, 1.431), highlighting the pronounced impact of shift work on adverse event occurrence. Moreover, our investigation unveiled a non-linear relationship between night shifts and adverse events. The probability of adverse events escalated with an increase in monthly night shifts. Interestingly, individuals working 5–6 night shifts per month exhibited a decreased probability of adverse events compared to those working 3–4 night shifts, followed by a subsequent rise with higher monthly shift frequencies. Furthermore, our analysis revealed that women demonstrated a lower probability of adverse events compared to men when considering an equivalent number of night shifts. Among different age groups, nurses aged 30–34 displayed the highest likelihood of experiencing adverse events.

It was observed that 325 cases (18.3%) were classified as adverse events in the past year in this study. The observed adverse event rate is higher than that reported in a recent study conducted in China, where the rate was 13.9% among operating room nurses [ 28 ]. However, it’s important to note that this difference may stem from variances in our target populations and measurement methodologies. Our study encompassed all nursing staff in the hospital, whereas the Chinese study specifically focused on operating room personnel. Furthermore, their definition of adverse events was narrower, limited to instances where physical damage was caused to the patient, whereas our definition of adverse events encompassed a broader range of scenarios.

Based on our findings, it appears that the frequency of night shifts for nurses is a risk factor for adverse events. This aligns with Muzio and his colleagues’ comprehensive systematic review examining the relationship between nursing shift work and clinical risk [ 48 ]. Their research revealed that, on average, night shift nurses slept for over an hour less during rest periods compared to their day shift counterparts. Additionally, workload and inadequate sleep were identified as the primary reasons for medical errors [ 48 ]. The results of this study also showed that the likelihood of adverse events increased with the number of night shifts per month, but those working 3–4 night shifts per month demonstrated a reduced likelihood of adverse events compared to those working 5–6 night shifts per month, which subsequently increased with the frequency of monthly shifts. This suggests that nurse managers should arrange for sufficient human resources to reduce the number of night shifts for nurses when arranging the frequency of night shifts as much as possible, but in clinical practice, due to human resource constraints, 5–6 night shifts per month is ideal and favourable to reduce the incidence of clinical adverse events.

In this study, we identified social support as a protective factor against adverse events, with an odds ratio (OR) of 0.991 (95% CI: 0.983, 0.999). This aligns with Khatatbeh’s research conducted in Jordan, which also found a noteworthy inverse relationship between adverse events and both familial and managerial support [ 49 ]. Our study further reinforces this idea and suggests that by enhancing overall support for healthcare professionals—particularly by fostering a healthier work environment for nurses—patient safety can be significantly improved [ 50 , 51 ]. This highlights the importance of social support in mitigating adverse events and emphasizes its potential to enhance patient safety and the quality of medical services.

Under the same number of night shifts, women demonstrate a lower probability of experiencing adverse events compared to men. This finding is not in alignment with the previous study conducted by Song and his colleagues among operating room nurses, which found no significant difference in the rate of adverse events between male and female nurses [ 26 ]. This discrepancy may be attributed to variations in sample design and research methodology. Thus, we must approach this finding with caution. Firstly, the uneven gender distribution in the nursing profession is an undeniable fact, with women comprising the majority. Consequently, this difference may largely reflect occupational characteristics, working environments, and job pressures, rather than gender itself. Although our study indicates that women exhibit a lower probability of experiencing adverse events under the same number of night shifts—potentially due to their more meticulous attitude towards their work, which may reduce the occurrence of such events—we cannot solely attribute this difference to inherent gender tendencies.

It is worth noting that among nurses of different age groups, those aged 30–34 face the highest probability of adverse events. Consistent with Saifuddin’s study, age is an important factor in the occurrence of adverse events [ 52 ]. We speculate that this may be due to specific circumstances faced by nurses in this age bracket. On one hand, they have accumulated a certain amount of work experience, which could potentially lead to complacency or carelessness. On the other hand, they may also be in a critical phase of their family life, such as having young children to care for, which could potentially distract them at work. In light of these findings, hospital policymakers should pay extra attention and provide additional support to nurses in this age group. The aim should be to reduce the likelihood of adverse events, ensuring high-quality service and patient safety within the hospital.

There are several limitations that need to be considered in this study. Firstly, the cross-sectional design limits the ability to extend results and infer causality. Secondly, participants were recruited using convenient sampling from only one distinct in China, which may limit the generalizability of the findings to a representative national sample. Thirdly, we recognize that reporting and recall biases may still be present in this study, such as adverse events. Despite our efforts to minimize these biases through standardized data collection tools and clear definitions of ‘adverse events’, some limitations remain. We suggest that future research could further investigate this matter using methods such as cohort studies to mitigate these biases. Fourthly, when discussing our research findings, we must acknowledge an important limitation: there is a discrepancy in the time frame of data collection between individual experiences of adverse events and other variables, such as depression, anxiety, and social support. This inconsistency in timing may have impacted our analysis, as data collected at different time points can reflect varying situations and individual states. To better understand the influence of this time difference on the results, we recommend that future research strictly control the time frame of data collection during the design phase, ensuring that all variables are assessed at the same or similar time points. This approach would not only improve data accuracy but also enhance the comparability of the study, leading to more reliable and effective conclusions. Despite this limitation in our research, we believe our findings provide valuable insights into the relationship between adverse events and mental health, offering important references for future studies.

Our study found that 325 cases (18.3%) were classified as adverse events over the past year, highlighting their prevalence and critical nature in nursing practice. The findings indicate a non-linear relationship between the frequency of night shifts and adverse events, suggesting a complex interplay of factors. Significant gender and age disparities were observed in the occurrence of adverse events, with women demonstrating lower susceptibility compared to men when exposed to equivalent numbers of night shifts. Nurses aged 30–34 exhibited the highest likelihood of experiencing adverse events. These insights have profound implications for clinical practice, nursing administration, and future research aimed at reducing adverse events in nursing. Future studies should further explore non-linear relationships between variables such as night shifts and adverse events in different contexts or populations. It is important to investigate these complex interplays of factors more comprehensively to develop targeted interventions and policies that safeguard nurse well-being and enhance patient care outcomes. Understanding these nuanced associations is crucial for advancing knowledge in this area and improving nursing practice.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

the multidimensional scale of perceived social support

The generalized anxiety disorder-7

the 9-item Patient Health Questionnaire

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Acknowledgements

We would like to thank all nurses who generously shared their time to participate in this survey.

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Mao xiaolan and Zhizhou Duan contributed equally.

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The Nursing department, The central hospital of enshi tujia and miao atunomous prefecture, Enshi, Hubei, China

Mao Xiaolan, Jianmei Jiang & Xiang Wei

Preventive health service, Jiangxi provincial people’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China

Zhizhou Duan

Department of Environmental Health, School of Public Health, Fudan University, 130 Dong’an Road, Shanghai, 200032, China

Zhiping Niu

Department of Biobank, Nantong First People’s Hospital, Nantong city, Jiangsu Province, China

Xiangfan Chen

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MX L, ZZ D analyzed the data and wrote manuscript; ZZ D and XF C revised the manuscript; ZP N, JM J, X W, and ZZ D edited the manuscript. All authors read and approved the final manuscript.

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Xiaolan, M., Duan, Z., Niu, Z. et al. Non-linear associations between night shifts and adverse events in nursing staff: a restricted cubic spline analysis. BMC Nurs 23 , 602 (2024). https://doi.org/10.1186/s12912-024-02259-3

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