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Record W4415593326 · doi:10.22454/primer.2025.174022

Methodology and New Avenues for the 2025 CERA Clerkship Director Survey

2025· article· en· W4415593326 on OpenAlexaboutno aff
Amanda Kost, Ray Biggs, Tiffany Ho

Bibliographic record

VenuePRiMER · 2025
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsOutreachProgram directorSurvey data collectionSurvey researchGraduate students

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.360
Threshold uncertainty score0.709

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.505
GPT teacher head0.567
Teacher spread0.062 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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