Regional differences in where and how family medicine residents intend to practise: a cross-sectional survey analysis
Bibliographic record
Abstract
Background: Family medicine residents choose among a range of practice options as they enter the physician workforce. We describe the demographic and personal characteristics of Canadian family medicine residents and examine differences in the intentions of residents from Ontario, Quebec, Western Canada and Atlantic Canada at the completion of their training, in terms of practice comprehensiveness, organizational model, clinical domains, practice settings and populations served. Methods: We analyzed national survey data collected by the College of Family Physicians of Canada and 16 university-based family medicine residency programs. We tabulated bivariable descriptive results and used logistic regression to estimate odds of practice intentions across regions, adjusting for family medicine resident characteristics. Results: Of 1680 respondents (61.5% of 2731 family medicine residents invited to participate), 66.3% (n = 1095) reported it was somewhat or highly likely they would commit to providing comprehensive care to the same group of patients within their first 3 years of practice. This percentage varied from 40.3% in Atlantic Canada to 85.1% in Ontario. In addition, 31.5% (n = 522) reported it was somewhat or highly likely they would focus only on specific clinical areas. Most respondents reported it was somewhat or highly likely that they would practise in a group physician practice (93.8%) or interprofessional team-based practice (88.1%), and only 7.7% expected to have a solo practice. Interpretation: Intentions for comprehensive and focused practice varied, but over 80% of family medicine residents indicated they intended to practise in a team-based model in all regions. Policy-makers and workforce planners should consider the impact of family medicine residents’ intentions on policy objectives.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".