Protocol for the 2023 CERA Clerkship Director Survey
Bibliographic record
Abstract
Introduction: CERA, the Council of Academic Family Medicine Educational Research Alliance, is a unique collaboration between multiple family medicine organizations to conduct omnibus surveys of distinct groups within family medicine. CERA’s vision is to support excellence in family medicine educational research and improve research skills in family medicine. This paper describes the methods of the 2023 Clerkship Directory Survey and presents the demographic results of survey respondents. Methods: CERA’s call for proposals for the annual Clerkship Directory Survey opened from January 2023 to February 2023. Five topics were selected, and authors of the selected proposals had a mentor assigned to their project. The survey was sent to Clerkship Directors via SurveyMonkey (Momentive, Inc) on May 30, 2023 and responses were collected through June 30, 2023. χ2 tests were used for descriptive analysis. Results: The survey was initially sent to 179 potential respondents but after receiving updated clerkship information, the final pool size was 169 (163 United States, 16 Canada). Ninety-six clerkship directors completed the survey, with a response rate of 56.80% (96/169). The demographic data of potential clerkship director respondents were compared with the demographic data of actual respondents. There were no significant difference in demographic data including location, gender, race/ethnicity and underrepresented in medicine status. Discussion: This paper describes the methods of the 2023 CERA Clerkship Directory Survey and shows that survey respondents are representative of clerkship directors. Authors of the five accepted survey topics are responsible for publishing their study findings.
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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 | 0.040 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.211 | 0.054 |
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".