National and provincial retention of medical graduates of Memorial University of Newfoundland
Bibliographic record
Abstract
BACKGROUND: Memorial University of Newfoundland (MUN) established its medical school in 1967 to meet the growing demand for physicians and alleviate the reliance on other Canadian and international medical schools for physicians. However, it is unclear how many of the graduates remained to practise in Canada and in Newfoundland and Labrador (NL). We conducted this study to identify the characteristics and predictors of MUN medical graduates working in Canada and NL after residency training. METHODS: We linked data from class lists, and alumni and postgraduate databases with data from the Southam Medical Database to determine 2004 practice locations for MUN graduates from 1973 to 1998. Multiple logistic regression analysis was used to identify predictors for working in Canada and in NL. RESULTS: Of the 1322 MUN graduates in our study, 1147 (86.8%) were working in Canada and 406 (30.7%) in NL in 2004. Predictors of physicians working in Canada included female sex (odds ratio [OR] 1.44, 95% confidence interval [CI] 1.01-2.04), being from Canada (OR 3.71, 95% CI 1.15-2.21), graduating in the 1980s (OR 1.52, 95% CI 1.02-2.24) and 1990s (OR 2.01, 95% CI 1.31-3.09) and having done some or all residency training at MUN (OR 1.59, 95% CI 1.53-9.01). Predictors of physicians working in NL included having a rural background (OR 1.37, 95% CI 1.04-1.81), being from NL (OR 9.23, 95% CI 5.52-15.44) and having done some or all residency training at MUN (OR 5.28, 95% CI 3.80-7.34). INTERPRETATION: The MUN medical school has made a substantial contribution to the local physician supply, producing over half the physicians working in the province in 2004. Initiatives to increase national and provincial retention of medical graduates include attracting rural students to medical careers, increasing admission of local students and providing incentives for graduates to complete their residency training in the province.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".