Unfulfilled promise, untapped potential: Feedback at the crossroads
Bibliographic record
Abstract
Feedback should be a key support for optimizing on-the-job learning in clinical medicine. Often, however, feedback fails to live up to its potential to productively direct and shape learning. In this article, two key influences on how and why feedback becomes meaningful are examined: the individual learner's perception of and response to feedback and the learning culture within which feedback is exchanged. Feedback must compete for learners' attention with a range of other learning cues that are available in clinical settings and must survive a learner's judgment of its credibility in order to become influential. These judgments, in turn, occur within a specific context--a distinct learning culture--that both shapes learners' definitions of credibility and facilitates or constrains the exchange of good feedback. By highlighting these important blind spots in the process by which feedback becomes meaningful, concrete and necessary steps toward a robust feedback culture within medical education are revealed.
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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 | 0.045 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.021 | 0.029 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".