Physician recruitment and retention in New Brunswick: a medical student perspective
Bibliographic record
Abstract
Background: Physician recruitment and retention is a priority for many Canadian provinces. Each province is unique in terms of recruitment strategies and packages offered; however, little is known about how medical students evaluate these programs. The purpose of the current study was to determine which factors matter most to New Brunswick (NB) medical students when considering their location of future practice. Method: A survey of NB medical students was conducted. Descriptive statistics were produced and a linear regression model was developed to study factors predictive of a student’s expressed willingness to practice in NB. Results: 158 medical students completed the online survey, which is a response rate of 55%. Job availability and spouse’s ability to work in the province were ranked as the top factors in deciding where to practice. In the final regression model, factors predictive of an expressed desire to practice in NB include being female, living in NB prior to medical school, attending medical school at Université de Sherbrooke, participation in the NB Preceptorship program, and a desire to practice family medicine. Conclusions: This study provides insight into what medical students consider when deciding where to practice. This research may be used to inform physician recruitment efforts and guide future research into medical education and policy.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".