Comparing Practice Intentions and Patterns of Family Physicians in Canada
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: In Canada, competency-based medical education prepares family medicine (FM) graduates to provide a broad scope of practice (SoP). We compared the practice intentions of FM residents at the end of training with actual practice patterns of early career family physicians (FPs) for SoP activities reflective of comprehensive family medicine. METHODS: We collected self-reported data from cross-sectional family medicine longitudinal surveys for exiting FM residents in 2015 and 2016 and from a separate cohort of FPs who were 3 years into practice in 2018 and 2019 from 15 programs. We measured outcomes from exiting FM residents intending to participate in SoP activities and FPs participating in 15 SoP domains of family medicine. RESULTS: A total of 1,409 exiting FM residents (58.2% response rate) and 523 early career FPs (21% response rate) responded to the surveys. A high correlation existed between the percentage of exiting residents who intended to participate in each SoP activity and the percentage of FPs who participated in those activities (r2=0.95). However, we found statistically significant declines in the percentage of FPs reporting involvement in the SoP activities compared to their reported practice intentions for 14 of the 15 domains. We saw the greatest declines in providing care in long-term care facilities, rural communities, emergency departments, intrapartum care, and care for Indigenous populations (P<.001). CONCLUSIONS: While SoP patterns are highly correlated with practice intentions, early-career FPs are less likely to provide care as intended for all SoP activities. Further research is needed on the factors influencing practice patterns in specific areas to determine how FP graduates can be supported to provide comprehensive 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".