Protocol for the 2023 CERA Department Chair Survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it