A review of the recruitment and retention strategies of rural and northern physicians in Manitoba, Canada
Bibliographic record
Abstract
Recruitment and retention of rural healthcare workers is an important health issue for almost all countries around the world. In 2010, the WHO released guidelines on the sorts of strategies and initiatives countries should implement to improve the number of rural physicians (1). These guidelines, along with recent literature from Canada and Australia, were explored for similar suggestions and themes. These themes were then used to examine the current recruitment and retention initiatives in Manitoba, Canada. Manitoba has over 300,000 citizens living in rural areas and over 100 physician vacancies (2, 3). Despite many new strategies, the shortage is on going (1). Therefore, the current strategies and initiatives in Manitoba are explored in detail and compared to what is recommended in the literature. From there, recommendations were made on how to improve the current deficit of rural physicians in Manitoba based on suggestions from the literature. To improve recruitment, more mandatory rural rotations, incorporation of rural health issues and topics into undergraduate and postgraduate curricula, and promotion of a rural lifestyle should be implemented. To improve retention, expanding continuing medical education or professional development programs in rural areas, implementation of a professional association, and support for physician’s families should be top priority. If Manitoba were to consider implementation of these strategies, rural physician recruitment and retention rates should improve in the next few years.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".