Medical learner perspectives on elements of an educational rural generalist pathway: survey outcomes
Bibliographic record
Abstract
INTRODUCTION: The Northern Ontario School of Medicine University (NOSM U) continues to be challenged in meeting its social accountability mandate of addressing the rural health human resource crises in its catchment of Northern Ontario. Its new educational initiative, the Rural Generalist Pathway (RGP) aims to graduate family physicians specifically prepared for rural practice. This study elicits the perspective of NOSM U learners on the various components being considered for this educational pathway. METHODS: A mixed methods survey was created for each of two medical learner groups, undergraduate NOSM U students and its family medicine residents. Quantitative data was analyzed for frequencies and percentages and qualitative data underwent thematic analysis. RESULTS: With a response rate of 24.6% for undergraduates and 37.9% for residents, the survey discovered undergraduates consider rural clinical rotations as the most valuable experiences in rural medicine. Among the findings, both the majority of medical students and residents (87.3% and 87.9% respectively) agreed that support for a resident's family well-being and community integration was the element of the pathway most likely to influence them in pursuing the RGP. Mentorship by a practicing rural physician was an element highly supported by 81% of undergraduate and 81.8% of postgraduate learners as likely to influence them to take the RGP. DISCUSSION: Incorporating learner perceptions into the development of the RGP could help focus institutional resources and enhance learner participation in this pathway, producing more rural family doctors to serve Northern Ontario.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".