Guidelines: the do’s, don’ts and don’t knows of feedback for clinical education
Bibliographic record
Abstract
INTRODUCTION: The guidelines offered in this paper aim to amalgamate the literature on formative feedback into practical Do's, Don'ts and Don't Knows for individual clinical supervisors and for the institutions that support clinical learning. METHODS: The authors built consensus by an iterative process. Do's and Don'ts were proposed based on authors' individual teaching experience and awareness of the literature, and the amalgamated set of guidelines were then refined by all authors and the evidence was summarized for each guideline. Don't Knows were identified as being important questions to this international group of educators which if answered would change practice. The criteria for inclusion of evidence for these guidelines were not those of a systematic review, so indicators of strength of these recommendations were developed which combine the evidence with the authors' consensus. RESULTS: A set of 32 Do and Don't guidelines with the important Don't Knows was compiled along with a summary of the evidence for each. These are divided into guidelines for the individual clinical supervisor giving feedback to their trainee (recommendations about both the process and the content of feedback) and guidelines for the learning culture (what elements of learning culture support the exchange of meaningful feedback, and what elements constrain it?) CONCLUSION: Feedback is not easy to get right, but it is essential to learning in medicine, and there is a wealth of evidence supporting the Do's and warning against the Don'ts. Further research into the critical Don't Knows of feedback is required. A new definition is offered: Helpful feedback is a supportive conversation that clarifies the trainee's awareness of their developing competencies, enhances their self-efficacy for making progress, challenges them to set objectives for improvement, and facilitates their development of strategies to enable that improvement to occur.
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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 | 0.116 | 0.425 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.017 | 0.011 |
| Research integrity | 0.033 | 0.035 |
| Insufficient payload (model declined to judge) | 0.012 | 0.014 |
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".