Work locations in 2014 of medical graduates of Memorial University of Newfoundland: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Part of the mandate for social accountability of medical schools is to address physician needs at the local, regional and national levels. We determined the work locations in 2014 of medical graduates of Memorial University of Newfoundland (MUN) and identified the characteristics and predictors of working in urban and rural areas of Canada and the province of Newfoundland and Labrador (NL). METHODS: We linked data from class lists, and alumni and postgraduate databases with data from the Scott's Medical Database to determine work locations in 2014 of MUN medical graduates from 1973 to 2008. Multiple logistic regression analysis was used to identify predictors of working in urban and rural areas of Canada and NL. RESULTS: Of the 1864 graduates in our study, 1642 (88.1%) were working in Canada, 638 (34.2%) in NL, 217 (11.6%) in rural Canada and 92 (4.9%) in rural NL in 2014. Predictors of physicians working in Canada included having a rural background, being from NL and graduating in the 1980s, 1990s or 2000s. Predictors of physicians working in NL included having a rural background, being from NL, graduating in the 2000s and having done some or all of their residency training at MUN. Having a rural background and being a family physician were predictors of working in rural Canada. Having a rural background, being from NL, having done some or all residency training at MUN and being a family physician were predictors of working in rural NL. INTERPRETATION: Most MUN graduates were working in Canada in 2014, with about one-third remaining in NL and much smaller percentages working in rural communities, especially in rural NL. These findings have implications for the physician supply in NL.
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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 | 0.003 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".