Protocol for the 2024 CERA Department Chair Survey
Bibliographic record
Abstract
Introduction: CERA, the Council of Academic Family Medicine (CAFM) Educational Research Alliance, is a program that provides an infrastructure for educational survey research. Members of the CAFM organizations can submit proposals to survey subgroups within academic family medicine. CERA's mission includes the production of rigorous medical education research as well as mentorship for newer researchers. The purpose of this article is to describe the methodology of the 2024 CERA Department Chair survey. Methods: The call for proposals for the survey was open from April 1-30, 2024. Ten proposals were received and five were accepted following a competitive peer-review process. The survey, which included questions from these five research teams as well as standard demographic questions, was approved by the American Academy of Family Physicians Institutional Review Baord. The sample was all chairs of departments of family medicine in the United States and Canada, as identified using member databases of CAFM organizations and responses to prior CERA surveys. The survey was then sent out via email using the Survey Monkey platform from August 13, 2024 through September 20, 2024. Results: The survey received 111 responses out of a population 218 potential participants, for a response rate of 50.92%. No significant differences were found for race/ethnicity, gender, age, or location between responders and the overall population. Conclusions: The 2024 CERA Department Chair Survey had an acceptable response rate, and no difference was found in demographic characteristics between responders and the overall population.
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 | 0.055 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.004 |
| Insufficient payload (model declined to judge) | 0.160 | 0.048 |
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".