Understanding the influence of medical education on physician geographic disposition: A qualitative study of family physician perspectives in Canada
Bibliographic record
Abstract
RATIONALE: Primary care access challenges are experienced by many communities. In several jurisdictions, including Canada, family physicians (FP) have the professional autonomy to organize their practice in alignment with professional and personal interests. Although system-level interventions are tremendously important, investment in upstream interventions associated with the medical education of graduating FPs is a promising strategy for ameliorating primary healthcare access challenges. AIMS AND OBJECTIVES: This study investigates the medical education experiences that influence FP's decisions about practice locations in Canada. METHODS: We conducted semistructured interviews with FPs who completed undergraduate and postgraduate medical training in Canada and now have a practice in Ontario, Canada. Interview data were coded and analysed using an unconstrained descriptive approach. RESULTS: FPs preferred practice locations are intimately tied to their desired practice scope. Practice preferences were shaped through training experiences with patient populations, heightened clinical responsibilities, practice models and locations, professional mentorships and networks. Proximity to family, partner and lifestyle preferences, cultural connections and the available practice opportunities also shaped practice location decisions. CONCLUSION: Medical education influences the identification and refinement of professional family practice preferences. Health workforce policies and interventions should leverage medical education to promote more equitable primary healthcare access.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".