Impact of a professional development session based on learner evaluations within a preceptor community of practice
Bibliographic record
Abstract
BACKGROUND: Physician preceptors play key roles in teaching medical professional trainees but receive little formal teacher training. One proposed way to improve teaching is by providing preceptors with learner feedback. The feedback from learner evaluations often has a limited impact with changes to teaching practice difficult to implement. This study explores the effect of using learner feedback to create a professional development session on teaching within a preceptor community of practice. METHODS: In this case study, 15 preceptors agreed to release their learner evaluations, and ten participated in the professional development session. Immediately and 2-3 months after the session, participants completed surveys on their intention to change and the changes made. The community of practice lead was interviewed to discuss the professional development session's impact. Qualitative approaches were used to analyze the data. RESULTS: From the learner evaluations, nine areas of improvement were identified and discussed. All attendees made changes to their teaching practices, which the community of practice lead confirmed. Fewer changes were identified at the community of practice group level. CONCLUSION: Using learner evaluations to structure a professional development session within a community of practice can help identify areas of improvement and create strategies to address these challenges.
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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 | 0.033 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".