The value of admissions characteristics for predicting the practice location of University of Saskatchewan College of Medicine graduates
Bibliographic record
Abstract
Background: The physician workforce in Saskatchewan depends upon the retention of locally trained physicians. Characteristics collected at the time of medical school application may predict future practice location, but these associations have not been explored. Methods: We identified the current practice location of University of Saskatchewan College of Medicine graduates who matriculated between 2000 and 2013 and extracted data from their admission applications including gender, age, high school, previous university, and current location at the time of application. We then conducted univariate and multivariate analyses to evaluate associations between these characteristics and rural- and Saskatchewan-based practice. Results: We identified the current practice location of 1,001 (98.9%) of the graduates of the included cohorts. Attending a Saskatchewan high school (p < 0.001), a high school in a smaller population center (p < 0.01), and a Saskatchewan university (p < 0.001) were predictive of Saskatchewan-based practice. Attending a high school outside of Saskatchewan (p < 0.05), a high school in a smaller population center (p < 0.001), and living in a small population centre at the time of application (p < 0.05) were predictive of rural-based practice within or outside of Saskatchewan. Conclusion: Demographic characteristics collected at time of medical school application are associated with future Saskatchewan- and rural-based practice. These findings will guide admissions policies in Saskatchewan and may inform admission practices of other medical schools.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".