Particularity, Engagement, Actionable Inferences, Reflexivity, and Legitimation tool for rigor in mixed methods implementation research

Abstract

Background

Implementation science helps generate approaches to expedite the uptake of evidence in practice. Mixed methods are commonly used in implementation research because they allow researchers to integrate distinct qualitative and quantitative methods and data sets to unravel the implementation process and context and design contextual tools for optimizing the implementation. To date, there has been limited discussion on how to ensure rigor in mixed methods implementation research.

Purpose

To present Particularity, Engagement, Actionable Inferences, Reflexivity, and Legitimation (PEARL) as a practical tool for understanding various components of rigor in mixed methods implementation research.

Data Sources

This methodological discussion is based on a nurse-led mixed methods implementation study. The PEARL tool was developed based on an interpretive, critical reflection, and purposive reading of selected literature sources drawn from the researchers' knowledge, experiences of designing and conducting mixed methods implementation research, and published methodological papers about mixed methods, implementation science, and research rigor.

Conclusion

An exemplar exploratory sequential mixed methods study in nursing is provided to illustrate the application of the PEARL tool. The proposed tool can be a useful and innovative tool for researchers and students intending to use mixed methods in implementation research. The tool offers a straightforward approach to learning the key rigor components of mixed methods implementation research for application in designing and conducting implementation research using mixed methods.

Clinical Relevance

Rigorous implementation research is critical for effective uptake of innovations and evidence-based knowledge into practice and policymaking. The proposed tool can be used as the means to establish rigor in mixed methods implementation research in nursing and health sciences.

Induction, deduction and abduction

Researchers often refer to the type of ‘reasoning’ that they have used to support their analysis and reach conclusions within their study. For example, Krick and colleagues completed a study that supported the development of an outcome framework for measuring the effectiveness of digital nursing technologies.1 They reported completing the analysis through combining ‘an inductive and deductive approach’ (p1), but what do these terms mean? How can these methods of reasoning support nursing practice, and guide the development and appraisal of research evidence?

This article will explore inductive and deductive reasoning and their place in nursing research. We will also explore a third approach to reasoning—abductive reasoning—which is arguably less well-known than induction and deduction, but just as prevalent and important in nursing practice and nursing research.

Burden among informal caregivers of individuals with heart failure: A mixed methods study

by Angela Durante, Ahtisham Younas, Angela Cuoco, Josiane Boyne, Bridgette M. Rice, Raul Juarez-Vela, Valentina Zeffiro, Ercole Vellone

To develop a comprehensive understanding of caregiver burden and its predictors from a dyadic perspective.

A convergent mixed methods design was used. This study was conducted in three European countries, Italy, Spain, and the Netherlands. A sample of 229 HF patients and caregivers was enrolled between February 2017 and December 2018 from the internal medicine ward, outpatient clinic, and private cardiologist medical office. In total, 184 dyads completed validated scales to measure burden, and 50 caregivers participated in semi-structured interviews to better understand the caregiver experience. The Care Dependency Scale, Montreal Cognitive Assessment, and SF-8 Health Survey were used for data collection. Multiple regression analysis was conducted to identify the predictors and qualitative content analysis was performed on qualitative data. The results were merged using joint displays.

Caregiver burden was predicted by the patient’s worse cognitive impairment, lower physical quality of life, and a higher care dependency perceived by the caregivers. The qualitative and mixed analysis demonstrated that caregiver burden has a physical, emotional, and social nature.

Caregiver burden can affect the capability of informal caregivers to support and care for their relatives with heart failure. Developing and evaluating individual and community-based strategies to address caregiver burden and enhance their quality of life are warranted.