Primary Care Tracks in Medical Schools
Bibliographic record
Abstract
INTRODUCTION: With the estimated future shortage of primary care physicians there is a need to recruit more medical students into family medicine. Longitudinal programs or primary care tracks in medical schools have been shown to successfully recruit students into primary care. The aim of this study was to examine the characteristics of primary care tracks in departments of family medicine. METHODS: Data were collected as part of the 2016 CERA Family Medicine Clerkship Director Survey. The survey included questions regarding the presence and description of available primary care tracks as well as the clerkship director's perception of impact. The survey was distributed via email to 125 US and 16 Canadian family medicine clerkship directors. RESULTS: The response rate was 86%. Thirty-five respondents (29%) reported offering a longitudinal primary care track. The majority of tracks select students on a competitive basis, are directed by family medicine educators, and include a wide variety of activities. Longitudinal experience in primary care ambulatory settings and primary care faculty mentorship were the most common activities. Almost 70% of clerkship directors believe there is a positive impact on students entering primary care. CONCLUSIONS: The current tracks are diverse in what they offer and could be tailored to the missions of individual medical schools. The majority of clerkship directors reported that they do have a positive impact on students entering primary care.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".