Learning culture and feedback: an international study of medical athletes and musicians
Bibliographic record
Abstract
OBJECTIVES: Feedback should facilitate learning, but within medical education it often fails to deliver on its promise. To better understand why feedback is challenging, we explored the unique perspectives of doctors who had also trained extensively in sport or music, aiming to: (i) distinguish the elements of the response to feedback that are determined by the individual learner from those determined by the learning culture, and (ii) understand how these elements interact in order to make recommendations for improving feedback in medical education. METHODS: Using a constructivist grounded theory approach, we conducted semi-structured interviews with 27 doctors or medical students who had high-level training and competitive or performance experience in sport (n = 15) or music (n = 12). Data were analysed iteratively using constant comparison. Key themes were identified and their relationships critically examined to derive a conceptual understanding of feedback and its impact. RESULTS: We identified three essential sources of influence on the meaning that feedback assumed: the individual learner; the characteristics of the feedback, and the learning culture. Individual learner traits, such as motivation and orientation toward feedback, appeared stable across learning contexts. Similarly, certain feedback characteristics, including specificity, credibility and actionability, were valued in sport, music and medicine alike. Learning culture influenced feedback in three ways: (i) by defining expectations for teachers and teacher-learner relationships; (ii) by establishing norms for and expectations of feedback, and (iii) by directing teachers' and learners' attention toward certain dimensions of performance. Learning culture therefore neither creates motivated learners nor defines 'good feedback'; rather, it creates the conditions and opportunities that allow good feedback to occur and learners to respond. CONCLUSIONS: An adequate understanding of feedback requires an integrated approach incorporating both the individual and the learning culture. Our research offers a clear direction for medicine's learning culture: normalise feedback; promote trusting teacher-learner relationships; define clear performance goals, and ensure that the goals of learners and teachers align.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".