Does Group Discussion of Student Clerkship Performance at an Education Committee Affect an Individual Committee Member???s Decisions?
Bibliographic record
Abstract
BACKGROUND: To determine whether deliberation as part of a group affects an individual's decisions for grading and remediation of marginal students. METHOD: In academic year 2001-02, members of a Department of Medical Education Committee prospectively completed pre- and postdiscussion surveys about their decision-making processes for third-year internal medicine clerkship students presented for marginal performance. Postdiscussion written comments were analyzed qualitatively. RESULTS: A total of 23 (14%) students were discussed, resulting in 297 individual committee member decisions (3,090 educator-minutes). A total of 76 of 297 (25%) decisions were altered following committee deliberations, changing the grade and/or remediation for nine students. Only seven of 76 (9%) changes were anticipated. Qualitative analysis revealed four underlying themes for changing: influence of members; data provided; clarification of process; and factors outside the clerkship. CONCLUSIONS: Group discussion influenced individual committee members' decisions for one-quarter of marginal students. The committee process allowed for clarification of the record, faculty development, and full discussion of student performance.
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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 | 0.127 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".