[The decentralized training program and the retention of general practitioners in Quebec's Lower St. Lawrence Region].
Bibliographic record
Abstract
OBJECTIVE: To assess the effect of decentralized training programs, financial incentives, and physicians' origins on whether general practitioners continue to practise in an area. DESIGN: Our data were obtained from the physician database maintained by the Bas-Saint-Laurent Regional Department of Health and Social Services and from responses to a mailed questionnaire completed by physicians in the study. SETTING: The Lower St. Lawrence Region of Quebec. PARTICIPANTS: General practitioners who practised in the area between 1985 and 2003. METHOD: We used the Cox proportional hazards model of survival analysis to ascertain which variables were related to retaining physicians in the area. RESULTS: The adjusted probability of physicians remaining in Bas-Saint-Laurent after being exposed to the area through rural rotations had an odds ratio of 2.12 (P = .15). The probability of remaining in the area climbed to an odds ratio of 4.5 (P < .01) for physicians originally from the Bas-Saint-Laurent region. Financial incentives appeared to make little difference to whether physicians were retained in the area. CONCLUSION: The most promising strategies for retaining rural general practitioners are recruiting candidates from rural areas and exposing medical students to rural practice through decentralized training.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".