Particularity, Engagement, Actionable Inferences, Reflexivity, and Legitimation tool for rigor in mixed methods implementation research
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.675 | 0.811 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.009 | 0.047 |
| Scholarly communication | 0.023 | 0.023 |
| Open science | 0.006 | 0.027 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".