Rural family medicine training site: Proposed framework.
Bibliographic record
Abstract
OBJECTIVE: To develop a framework for a successful rural family medicine training program and to assess the potential for a rural family medicine residency training program using the Weyburn and Estevan areas of Saskatchewan as test sites. DESIGN: A mixed-method design was used; however, the focus of this article was on the qualitative data collected. Questions formulated for the semistructured interviews evolved from the literature. SETTING: Rural Saskatchewan. PARTICIPANTS: Community physicians and representatives from the Sun Country Regional Health Authority, the Saskatchewan Ministry of Health, and the University of Saskatchewan. METHODS: The data were documented during the interviews using a laptop computer, and the responses were reviewed with participants at the end of their interviews to ensure accuracy. The qualitative data collected were analyzed using inductive thematic analysis. MAIN FINDINGS: Through the analysis of the data several themes emerged related to implementing a rural family medicine residency training program. Key predictors of success were physical resources, physician champions, physician teachers, educational support, administrative support, and other specialist support. Barriers to the development of a rural family medicine training site were differing priorities, lack of human resources, and lack of physical resources. CONCLUSION: A project of this magnitude requires many people at different levels collaborating to be successful.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".