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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".