Developing a framework for medical student feedback literacy using a triangulated thematic analysis
Bibliographic record
Abstract
INTRODUCTION: Feedback literacy is critical for medical students to develop their competencies. However, a conceptualisation or framework of feedback literacy specific to the medical education context has yet to be developed. A comprehensive framework that addresses diverse feedback sources, dynamic environments, and the prioritisation of patient safety can guide the development of feedback literacy in medical students. MATERIALS AND METHODS: The present study explored and triangulated stakeholder perspectives (medical students, medical educators and patients) on medical student feedback literacy through semi-structured interviews. This information was then used to develop a feedback literacy framework. Data collection took place over approximately six months, from August 2023 to January 2024. RESULTS: Nine focus group discussions and 26 individual interviews were conducted, involving 38 medical students from 14 medical schools, 15 medical educators from six medical schools and 11 patients from four healthcare facilities. The resulting transcripts were thematically analysed, and stakeholder perspectives were triangulated to identify emerging patterns. A feedback literacy framework for medical students was then designed based on the coded themes. The framework comprises seven elements, including a fundamental appreciation of feedback, preparation for and engagement in the process, analysis and evaluation of information and the need for emotional regulation to effectively internalise feedback and become safe, competent doctors. CONCLUSION: This study builds on existing frameworks to reveal novel elements of feedback literacy. Patient safety and reflective practice emerged as new components of feedback literacy exclusive to medical students, while emotional regulation and the use of multiple feedback sources were adapted to fit the medical education context. Thus, this framework addresses an identified gap and advances the conceptualisation of feedback literacy to suit medical students better.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| 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) | 0.001 | 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".