Systematic review of feedback literacy instruments for health professions students
Bibliographic record
Abstract
Successfully managing and utilizing feedback is a critical skill for self-improvement. Properly identifying feedback literacy level is crucial to facilitate teachers and learners especially in clinical learning to plan for better learning experience. The present review aimed to gather and examine the existing definitions and metrics used to assess feedback literacy (or parts of its concepts) for health professions education. A systematic search was conducted on six databases, together with a manual search in January 2023. Quality of the included studies were appraised using the COSMIN Checklist. Information on the psychometric properties and clinical utility of the accepted instruments were extracted. A total 2226 records of studies were identified, and 11 articles included in the final analysis extracting 13 instruments. These instruments can be administered easily, and most are readily accessible. However, 'appreciating feedback' was overrepresented compared to the other three features of feedback literacy and none of the instruments had sufficient quality across all COSMIN validity rating sections. Further research studies should focus on developing and refining feedback literacy instruments that can be adapted to many contexts within health professions education. Future research should apply a rigorous methodology to produce a valid and reliable student feedback literacy instrument.
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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 | 0.026 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".