Starting out rural: a qualitative study of the experiences of family physician graduates transitioning to practice in rural Ontario
Bibliographic record
Abstract
BACKGROUND: New family medicine graduates are a promising group to recruit to underserved rural areas. This study aimed to understand the experiences of this group as they transitioned to practice in rural Ontario. METHODS: We used a hermeneutic phenomenology approach. Purposive sampling was used to recruit participants who graduated from a Canadian family medicine residency program and worked in a rural community in Ontario (Rurality Index for Ontario score ≥ 40) for at least 1 year within the past 5 years. Participants completed an online demographic survey followed by a virtual semistructured interview (May-August 2022). Interviews were video recorded and transcribed. Two researchers reviewed transcripts for codes, and then codes were reviewed in an iterative process to create themes. Transcripts, codes and themes were reviewed by an independent researcher, and final themes were shared with participants to ensure reliability. RESULTS: We included 18 family physicians in the study. We identified 8 themes and 18 subthemes. The themes identified as important to the experience of new graduates were as follows: choosing rural practice, preparedness for practice, navigating work-life balance, navigating transition to practice, challenges during transition to practice, successes during transition to practice, locuming and emergency medicine as part of rural generalist practice. INTERPRETATION: Most physicians interviewed felt prepared for rural practice and enjoyed their work; however, they faced unique challenges associated with being an early-career physician in rural practice. This study identifies opportunities for improvements, which can guide medical educators, rural communities and their recruiters, new graduates and policy-makers.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.003 | 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".