Bibliographic record
Abstract
<h3>Abstract</h3> <h3>Objective</h3> To describe the relationships between rural practice and the personal and medical education characteristics of medical students and residents. <h3>Design</h3> Cross-sectional, mailed survey. <h3>Setting</h3> Manitoba. <h3>Participants</h3> Of 2578 physician graduates of the University of Manitoba from 1965 to 2000 who were surveyed, 1269 (49%) responded. <h3>Main outcome measures</h3> Whether physicians had ever practised in rural settings, and their demographic characteristics and adolescent, medical school, and residency training experiences. Multivariate logistic regression models were used to determine variables jointly and independently associated with rural practice. <h3>Results</h3> Of 1269 respondents, 39% had practised in rural settings, including 58% of the 362 respondents who identified family practice as their primary career activity, and 32% of the 907 respondents whose primary activities were other than family practice. For all graduates, being male (<i>P</i> = .0289), having lived in a rural community (<i>P</i> < .0001), having attended a rural high school (<i>P</i> < .0001), and having rural educational experiences during medical school (<i>P</i> = .0068) or during postgraduate training (<i>P</i> < .0001) were significantly related to a greater likelihood of rural practice. In the final multivariate model, graduates of rural high schools, compared with those from urban public schools, were 1.57 times (95% CI 1.09 to 2.26) more likely to have practised in rural settings. Graduates who undertook part of their undergraduate training in rural settings were 1.34 times (95% CI 1.09 to 1.75) more likely to practise in rural locations. For both undergraduates and residents, the distance of their rural education experiences from Winnipeg and the likelihood of rural practice were directly related. For both FPs and non-FPs, being male and undertaking rural education during residency training were associated with a greater likelihood of rural practice, as was the distance of the training experience from the urban setting. For non-FPs a similar association was observed with undergraduate rural experiences. <h3>Conclusion</h3> This large survey of graduates from a Canadian medical school demonstrated that attending a rural high school (<i>P</i> < .0001) and having rural educational exposure during medical school and residency training (<i>P</i> = .0068) were significantly associated with a physician practising in a rural location. That is, rural educational experiences on the continuum from high school through residency training appeared to be associated with rural practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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; both teacher heads agree on what is shown here.
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".