Why they leave: Small town rural realities of northern physician turnover
Bibliographic record
Abstract
INTRODUCTION: This study seeks to explore influential factors leading to physician turnover in designated Rural Northern Physician Group Agreement (RNPGA) communities in Northern Ontario, as well as physician's perceptions of the RNPGA contract and effects of the Northern Ontario School of Medicine (NOSM) on physician retention in these communities. METHODS: Twelve qualitative semi-structured interviews were completed with rural physicians who had RNPGA contracts within the past 5 years but had left their practice community. Data collected from recorded interviews were analysed using a thematic analysis approach in order to identify common themes. RESULTS: A range of factors influencing physician's decisions to leave were identified including lack of partner career prospects, burnout and lack of opportunities and amenities. Common challenges were sometimes also perceived as rewards of rural practice. The concern of lack of flexibility of the RNPGA contract was identified, as well as a perceived lack of presence of NOSM graduates in RNPGA communities. CONCLUSION: A variety of factors influence physician turnover in RNPGA communities. These may be considered by communities hoping to inform recruitment and retention policy. Renewal of the RNPGA contract may require consideration for availability of part-time positions, increasing the number of physicians funded and incentivising physician wellness. NOSM may consider mandatory postgraduate programme placements in RNPGA communities and further development of infrastructure in these communities to improve learner, graduate and institutional engagement.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".