What Are the Discussion Topics and Usefulness of Clerkship Directorsʼ Meetings Within Medical Schools? A Report From the CDIM 2007 National Survey
Bibliographic record
Abstract
PURPOSE: To characterize meetings among clerkship directors within medical schools, specifically with regard to topics of discussion, and usefulness of the meetings. METHOD: In 2007, the Clerkship Directors in Internal Medicine surveyed its institutional members from 114 U.S. and Canadian medical schools. Respondents were asked about the frequency of meetings among clerkship directors, the topics of discussions, whether they were precluded from discussing students in academic difficulty, and the benefits and drawbacks of discussing students' performance. Analysis included descriptive statistics and qualitative analysis of free-text responses. RESULTS: The response rate was 71% (81/114). The most common meeting frequencies were monthly (77%) or quarterly (15%). Topics discussed included deans' policies (91%), general announcements (90%), recommendations from clerkship directors to the dean (86%), Liaison Committee on Medical Education site visit preparation (84%), curricular input (82%), discussion of struggling students (49%), students' progress (48%), and planning for at-risk preclerkship students (22%). Some respondents (16%) were explicitly prevented from discussing student performance, for reasons of possible harm to the student (30/84; 36%), bias developing against the student (13/84; 16%), violation of privacy/lack of student confidentiality (4/84; 5%), and possible bias in grading or evaluation (8/84; 10%). Most respondents (94%) agreed there were benefits to students resulting from discussions: longitudinal tracking of concerns, designing remediation, tailoring teacher assignments, and societal obligations. CONCLUSIONS: Clerkship directors meet frequently to discuss curriculum, policy, and students' performance. Most internal medicine clerkship directors believe discussing students' performance helps design educational interventions that balance societal obligations with student confidentiality.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".