Methodology and New Avenues for the 2025 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 that conduct omnibus surveys of different academic family medicine groups. This paper describes the methodology of the 2025 Clerkship Director (CD) Survey and demographic results of respondents. Methods: Four topics for the annual CD survey were selected via peer review after a call for proposals in early 2025. The survey was sent to clerkship directors via email from June 3, 2025 to July 11, 2025. The demographics of the sampling frame vs sample were compared with χ2 tests to determine if they were representative. Results: One hundred eighty surveys were sent out; after receiving updated clerkship information, the final 2025 pool size was 174 survey recipients (161 in the United States and 13 in Canada). Although there are 43 DO schools in the US, the CD list maintained by STFM lacks these CDs. One hundred CDs responded for a response rate of 57.47%. We compared demographic data of the sampling frame with the sample. There were no significant differences in location, gender, or race/ethnicity. There was a significant difference in underrepresented in medicine status and being a physician. Discussion: 2025 CD Survey respondents are representative of CDs. Few CAFM organization members submit to this survey and DO schools have not historically been included so are not represented. Targeted outreach to DO schools to identify their CD is planned prior to the launch of the 2026 CD survey.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.051 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".