Improving the recruitment and retention of doctors by training medical students locally
Bibliographic record
Abstract
CONTEXT: The global shortage of doctors is of concern. This is particularly true in French-speaking regions of New Brunswick, Canada, where there is no medical school. Since 1981, francophone medical students from New Brunswick have been able to undertake part of their training in their province through an agreement with medical schools in another province. We studied the effects of frequency and length of exposure to the province of origin during medical training on the likelihood that a doctor will ever or currently practise medicine in that province. METHODS: A questionnaire was sent to 390 francophone doctors from New Brunswick to collect information on history of medical training and practice. Multivariate logistic regressions were used to identify whether exposure to New Brunswick during medical training at the undergraduate and postgraduate levels affects the likelihood of ever or currently practising in the province. RESULTS: A total of 263 doctors participated. Among family doctors, those with exposure to their province of origin in 1, 2, 3 or 4 years of undergraduate training were 2.5 (95% confidence interval [CI] 0.8-7.4), 2.5 (95% CI 0.7-8.6), 9.3 (95% CI 1.5-56.9) and 9.3 (95% CI 1.4-60.1) times more likely, respectively, to currently practise in New Brunswick than doctors who had experienced no exposure to the province during undergraduate training. Among specialty doctors, exposure to New Brunswick during undergraduate training had no effect on location of practice. Family and specialty doctors who had been exposed to New Brunswick during postgraduate residency were 5.9 (95% CI 2.3-14.9) and 3.2 (95% CI 0.9-11.6) times more likely, respectively, to practise in the province than doctors without postgraduate exposure. CONCLUSIONS: Greater exposure to New Brunswick during medical training is associated with significantly better odds that doctors will be recruited to and retained in the province. Some effects are perceived for exposure during both undergraduate (most importantly in the final years) and postgraduate programmes.
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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 | 0.027 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".