Early-career work location of Memorial University medical graduates: Why the decline in rural practice?
Bibliographic record
Abstract
INTRODUCTION: In a previous study, we found a decline in the proportion of Memorial University of Newfoundland (MUN) medical alumni practising in rural areas, particularly in Newfoundland and Labrador. The current study focused on the work location of recent graduates and examined the predictors of working in rural Canada and in rural Newfoundland and Labrador within the first 15 years following graduation. METHODS: We linked data from graduating class lists and the alumni and postgraduate databases with Scott's Medical Database to create a record of all graduates from 1973 to 2008, including their work location. We identified differences and significant predictors for each outcome and then described and compared the characteristics of 4 cohorts of graduating classes. RESULTS: In their early career, 127/1113 (11.4%) MUN medical graduates were working in rural Canada, and 57 (5.1%) were working in rural Newfoundland and Labrador. Having a rural background and being a family physician were predictors of working in rural Canada, and having a rural background, doing at least part of the residency at MUN, being from Newfoundland and Labrador and being a family physician were predictors of working in rural Newfoundland and Labrador. Seventy-four (13.6%) and 33 (6.1%) of 1989-1998 graduates worked in rural Canada and rural Newfoundland and Labrador, respectively, compared to 53 (9.3%) and 24 (4.2%), respectively, of 1999-2008 graduates. CONCLUSION: The proportion of MUN medical graduates who worked in rural communities early in their career decreased among recent cohorts. The results show the impact of changes in the characteristics of MUN medical graduates, who increasingly opt for specialist practice and residency training outside the province, and the important role of local postgraduate training.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".