Upholding the principles of primary care in preceptors' practices.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Family medicine preceptorships are crucial to educating future physicians, but there is a lack of research on how well preceptors are following the principles of primary care. This study used the Primary Care Assessment Tool (PCAT)-Provider Edition to determine how well medical preceptors provide quality medical care. METHODS: A total of 134 family medicine preceptors in the Maritime provinces of Canada answered questions about their practice behaviors, along with background information about themselves, their practice, and their practice population. RESULTS: The highest scores were for "coordination: integration of care," and the lowest were for "cultural competence." PCAT scores improved with the number of patients seen weekly. Scores for first contact accessibility were higher for females and for those with 11-20 years experience as a preceptor, who saw more patients weekly, and in urban centers. "Longitudinality: relationship" scores were higher among those with at least 11 years of practice experience and who saw more patients weekly. "Community orientation" scores were higher for preceptors who saw more patients weekly and accepted new patients. "Cultural competence" scores were higher for preceptors with a culturally diverse practice population and who accepted new patients. "Coordination: integration of care" scores were higher among rural practices. "Coordination: medical records continuity" scores were higher in practices with less than 5 years' experience. CONCLUSIONS: Maritime preceptors report providing quality primary care, and the PCAT can be used to benchmark the quality of primary care provided by preceptors.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".