Using a Novel Small-Group Approach to Enhance Feedback Skills for Community-Based Teachers
Bibliographic record
Abstract
BACKGROUND: As medical education expands into distant settings, challenges in providing faculty development to busy clinical teachers increase-especially for those who have difficulty accessing sessions offered at academic centers. DESCRIPTION: Sixty-five clinical teachers participated in six small-group workshops, using a printed module on the topic of delivering feedback. The modules included teaching-learning "cases," tools, and a summary of medical literature. The group facilitator did not require expertise in delivering feedback. Surveys inquired about impact immediately after the session and at 3 months. EVALUATION: Analysis confirmed that participants found the workshop format valuable, and the majority committed to making changes in their approaches to providing feedback. At follow-up, most participants reported that planned changes had been implemented. CONCLUSIONS: A low-tech approach to faculty development, using facilitated small-group discussion of a specially prepared educational module, is feasible for any site and can enhance teaching approaches in both urban and rural practice settings.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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".