Methodology, Respondents, and Past Topics for 2024 CERA Clerkship Director Survey
Bibliographic record
Abstract
Introduction: CERA, the Council of Academic Family Medicine (CAFM) Educational Research Alliance, is a collaboration between four family medicine organizations to conduct omnibus surveys of different groups within family medicine. This article describes the methodology of the 2024 Clerkship Directory (CD) Survey, presents the demographic results of respondents, and categorizes CD topics from 2012 through 2024. Methods: tests to compare the demographics of sampling frame against the sample to determine if they were representative of the sampling frame. We used program records to describe past survey topics. Results: One hundred seventy-nine surveys were sent out; after receiving updated clerkship information, the final 2024 pool size was 173 survey recipients (158 in the United States and 15 in Canada); 91 clerkship directors completed the survey, with a response rate of 52.60% (91/173). We compared demographic data of sampling frame with the sample. There was no significant difference in demographics including location, gender, race/ethnicity, underrepresented in medicine status, or MD degree. CD survey topics from 2012-2024 included 6 on preceptors, 29 on content/curriculum, 8 on grading/assessment, 8 on administration, and 9 on other. Discussion: 2024 Clerkship Directory Survey respondents are representative of clerkship directors. From 2012-2024 the most studied topic was content/curriculum. The Clerkship Director Survey continues to offer important scholarship opportunities and insights into current themes in undergraduate medical education.
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| 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) | 0.023 | 0.014 |
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".