Trajectories of physicians in Manitoba, Canada: the influence of contact points of rural-focused professional learning
Bibliographic record
Abstract
BACKGROUND: The Manitoba Office of Rural and Northern Health (ORNH) provided a multi-year series of elective opportunities for undergraduate medical students to support rural/remote medical practice. The purpose of this study was to examine the career trajectories of Manitoba physicians in eight matched cohorts over the period 2004-2007 between: 1) those who experienced a required rural clinical block rotation only during their undergraduate medicine training in Manitoba (Med 1 and Med 3), and; 2) those who engaged in and completed additional elective programs referred to here as "contact points". METHODS: The study utilized a retrospective/longitudinal matched cohort design which included the common factor of a mandated rural clinical one-week rotation and the differentiating factors of experiences in elective programming offered by the ORNH (contact points). RESULTS: ) to have continued contact with ORNH in addition to the mandatory rural rotation alone. For practitioners now located in rural/remote settings, a mean of 0.903 contact points per learner with ORNH programs is observed. For those now in urban practice the mean number of contact points per learner was 0.233. CONCLUSION: We conclude that there is an association between rural-focused contact points and rural and remote practice in Manitoba. Targeted professional learning where physician recruitment and retention remains a continuing challenge is discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".