Protocol for the 2023 CERA Department Chair Survey
Bibliographic record
Abstract
Introduction: CERA, the Council of Academic Family Medicine Educational Research Alliance, is a program sponsored by the academic family medicine organizations with the goal of supporting and improving educational research in family medicine. CERA produces surveys of different groups in academic family medicine, including an annual survey of department chairs, and members can apply to add their question sets to these surveys. This article describes the methods and demographics of the 2023 CERA Department Chair Survey. Methods: The call for proposals for the CERA Department Chair Survey was open from April 3, 2023 through May 9, 2023. Fifteen proposals were received, and five were accepted for the final survey based on scoring by peer reviewers. The Institutional Review Board of the American Academy of Family Physicians approved the survey. The final survey, including question sets from five research teams and standard demographic questions, was sent to 227 department chairs in the United States and Canada. Results: Overall, 114 chairs responded to the survey, for a response rate of 50.2%. Demographic variables, including race/ethnicity, gender, age, and region of the country, did not differ between respondents and nonrespondents. Discussion: The CERA Department Chair Survey provides a framework for members of academic family medicine organizations to conduct survey research on topics that are important to the specialty. Advantages of the CERA process include a national sample and robust response rate. Disadvantages are primarily the limitation in number of survey questions and the fact that not all proposals are accepted.
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 | 0.058 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.177 | 0.051 |
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".