Challenges and future directions in the measurement of evidence‐based practice: Qualitative analysis of umbrella review findings
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: An important aspect of scholarly discussions about evidence-based practice (EBP) is how EBP is measured. Given the conceptual and empirical developments in the study of EBP over the last 3 decades, there is a need to better understand how to best measure EBP in educational and clinical contexts. The aim of this study was to identify and describe the main challenges, recommendations for practice, and areas of future research in the measurement of EBP across the health professions as reported by systematic reviews (SRs). METHODS: We conducted a secondary analysis of qualitative data obtained in the context of a previously published umbrella review that aimed to compare SRs on EBP measures. Two reviewers independently extracted excerpts from the results and discussion/conclusion sections of the 10 included SRs that aligned with the three research aims. An iterative six-phase reflexive thematic analysis according to Braun and Clarke was conducted. RESULTS: Our thematic analysis produced five themes describing the main challenges associated with measuring EBP, four themes outlining main recommendations for practice, and four themes representing areas of future research. Challenges include limited psychometric testing and validity evidence for existing EBP measures; limitations with the self-report format; lack of construct clarity of EBP measures; inability to capture the complexity of the EBP process and outcomes; and the context-specific nature of EBP measures. Reported recommendations for practice include acknowledging the multidimensionality of EBP; adapting EBP measures to the context and re-examining the validity argument; and considering the feasibility and acceptability of measures. Areas of future research included the development of comprehensive, multidimensional EBP measures and the need for expert consensus on the operationalization of EBP. CONCLUSIONS: This study suggests that existing measures may be insufficient in capturing the multidimensional, contextual and dynamic nature of EBP. There is a need for a clear operationalization of EBP and an improved understanding and application of validity theory.
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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 | 0.309 | 0.382 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".