Describing Broad Scope Comprehensive Care Practice Intentions of Canadian FM Graduates-Does Exposure Matter?
Bibliographic record
Abstract
Context: Concerns have been raised that family physicians and new graduates are narrowing their scope of practice. The College of Family Physicians of Canada has described comprehensive care in the Family Medicine Professional Profile as that which includes primary care, in-hospital, maternity, emergency, and long-term/home care delivered collectively by family physicians. Training experiences during residency are designed to expose residents to these domains of care, with a level of competence demonstrated that that supports readiness to practice safely We explore family medicine residents’ intention to practice comprehensive care over 5 years and consider if exposure is associated with practice intention. Objective: Assess trends in the comprehensive care intentions of FM residents at beginning and end of residency and level of exposure during residency. Study Design: Secondary analysis of de-identified aggregate survey data. Setting or Dataset: The Family Medicine Longitudinal Survey Population studied: All FM residents entering (n=5045) or exiting (n=4295) FM residency in Canada from 2017-2021. Outcome Measures: 5-point Likert scales assessing intention to practice and comprehensive care domains. Results: At entry to residency, residents’ intentions to practice in the domains of intrapartum care, palliative care, care in the home, and long term care very modestly declined (r ≥ -.05) over time. Intentions to provide care for Indigenous populations increased (r = .09). Practice intention to provide office based clinical procedures, care in the home, and long term care at end of residency very modestly declined (r ≥ -.07). Intentions to provide care for marginalized populations very modestly increased (r = .05). For many of these domains, intention to practice is significantly higher (p ≤ .001) with higher exposure. Conclusions: Our findings suggest there may be a small decline over time in intention to practice comprehensive care at both entry to and exit from residency and that higher levels of exposure is related to higher levels of intention to practice in many comprehensive care domains. Improving exposure during training could be one factor for programs to address declining practice intention in identified domains of 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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".