What we've got here is failure to communicate: Exploring perceptions of how much feedback is happening in clinical workplace teaching
Bibliographic record
Abstract
Abstract Introduction: Feedback is invaluable in helping learners improve their performance and clinical competence, but studies have historically documented contrasting perspectives between learners and teachers in how much feedback is given by teachers to learners in clinical training. We explore why there is a discrepancy between learner and teacher perceptions of the feedback that is shared in a clinical teaching encounter. Methods: We recruited 23 preceptors (clinical teachers) from a mid-size Canadian medical school that has a diverse group of generalist and focused specialties. We used inductive content analysis to explore preceptors’ perceptions of both how much feedback they shared with learners, as well as amount of feedback that they believe learners would report was shared. Results: Analysis of interviews generated two themes: (i) difficulty among preceptors in quantifying the feedback they share to learners, and; (ii) discrepancies between preceptors in the definition of feedback. Discussion: The key themes identified in this study highlight that preceptors’ varying definitions of feedback and their difficulty in ascertaining how much feedback they share with learners can be attributed to a lack of a common understanding of feedback. When engaging in a feedback conversation, both the teacher and the learner engage in a meaning-making process that yields a shared understanding that feedback is occurring, and that information offered by the teacher is aimed at supporting the learner. We recommend that both faculty development sessions and educational sessions with learners should incorporate early check-ins to ensure a shared understanding of the definition of feedback.
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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 | 0.025 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".