Paying Community Preceptors in the Family Medicine Clerkship: Trends From a CERA Secondary Analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Recent evidence has suggested that fewer family medicine clerkships are using community preceptors as their primary source of teaching. One way to incentivize community preceptors is to pay them. Multiple factors, though, make payment of community preceptors complex. Our study looked at trends in payment of community preceptors in the United States and Canada over the past decade. METHODS: We performed a secondary analysis of the Council of Academic Family Medicine Educational Research Alliance annual family medicine clerkship director surveys from 2014 to 2023. We analyzed the surveys' standard clerkship payment questions and identified trends using Pearson's correlation coefficient test. RESULTS: From 2014 to 2023, we found no significant change in the number of medical schools that pay community preceptors in the family medicine clerkship. We also found no significant change in the amount paid to community preceptors in the family medicine clerkship. In 2014, the average amount paid was $238±138 per student, compared to 2023 where the average amount paid was $258±$158. When analyzed against inflation, these data reflect that a significant gap in the value of the compensation for community preceptors has developed. CONCLUSIONS: The compensation of community preceptors in US and Canadian family medicine clerkships has not kept up with inflation. Additional research is needed to study the motivations of community preceptors to educate in the family medicine clerkship and how both monetary and nonmonetary incentives can help recruit and retain these important educators.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".