Creation and validation of the PERFECT: a critical incident tool for evaluating change in the practices of health professionals
Bibliographic record
Abstract
RATIONALE: The critical incident technique provides a means to better understand the reasons behind clinicians' practices and changes in practice. No standardized tool exists to elicit information using this technique. OBJECTIVES: To create and validate a standardized tool that explores change and reasons for change in professional practice. METHOD: Item generation was based on expert consultation and a review of the clinical practice and knowledge translation literature. The draft tool was pilot-tested with a convenience sample of 10 rehabilitation clinicians to receive feedback on its content, clarity, optimal cueing, omissions and ease of recall of critical incidents. RESULTS: The tool was progressively refined and validated according to feedback from both the clinicians and expert reviewers. The final version of the tool includes 33 questions designed to elicit information on change and reasons for change in four areas: problem identification, assessment, treatment and referral practices. In addition, it elicits information on factors that facilitate or hinder change in practice. Cues are included when necessary to clarify questions and facilitate responses. Regarding ease of recall, all clinicians confirmed that beginning with a 6-month recall of practice change and working back to 1 year was a facilitator. All clinicians mentioned that the tool encouraged them to reflect about changes they made in their practice or lack thereof. CONCLUSION: The newly created standardized critical incident tool, named the PERFECT (Professional Evaluation & Reflection on Change Tool) provides an opportunity for widespread applicability to explore change, reasons for change, as well as facilitators and barriers to change in the practices of health professionals.
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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 | 0.120 | 0.248 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".