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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".