Family medicine education in rural communities as a health service intervention supporting recruitment and retention of physicians: Advancing Rural Family Medicine: The Canadian Collaborative Taskforce.
Bibliographic record
Abstract
OBJECTIVE: To develop a pan-Canadian rural education road map to advance the recruitment and retention of family physicians in rural, remote, and isolated regions of Canada in order to improve access and health care outcomes for these populations. COMPOSITION OF THE TASK FORCE: Members of the task force were chosen from key stakeholder groups including educators, practitioners, the College of Family Physicians of Canada education committee chairs, deans, chairs of family medicine, experts in rural education, and key decision makers. The task force members were purposefully selected to represent a mix of key perspectives needed to ensure the work produced was rigorous and of high quality. Observers from the Canadian Medical Association and Health Canada's Council on Health Workforce, and representatives from the Royal College of Physicians and Surgeons of Canada, were also invited to provide their perspectives and to encourage and coordinate multiorganization action. METHODS: The task force commissioned a focused literature review of the peer-reviewed and gray literature to examine the status of rural medical education, training, and practice in relation to the health needs of rural and remote communities in Canada, and also completed an environmental scan. REPORT: The environmental scan included interviews with more than 100 policy makers, government representatives, providers, educators, learners, and community leaders; 17 interviews with practising rural physicians; and 2 surveys administered to all 17 faculties of medicine. The gaps identified from the focused literature review and the results of the environmental scan will be used to develop the task force's recommendations for action, highlighting the role of key partners in implementation and needed action. CONCLUSION: The work of the task force provides an opportunity to bring the various partners together in a coordinated way. By understanding who is responsible and the actions each stakeholder needs to take to make the recommendations a reality, the task force can lay the groundwork for developing a coordinated, comprehensive health human resource strategy that considers the integral role of medical education as a health system intervention.
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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 | 0.024 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".