The rural road map for action: an examination of undergraduate medical education in Canada
Bibliographic record
Abstract
Background: There is currently a maldistribution of physicians across Canada, with rural areas facing a greater physician shortage. The taskforce between the College of Family Physicians and the Society of Rural Physicians created a report, "The Rural Road Map for Action" (RRMA) to improve rural Canadians' health by training and retaining an increased number of rural family physicians. Using the RRMA as a framework, this paper aims to examine the extent to which medical schools in Canada are following the RRMA. Methods: Researchers used cross-sectional survey and collected data from 12 of 17 medical school undergraduate Deans from across Canada using both closed and open ended survey questions. Results were analyzed using quantitative (frequencies) and qualitative methods (content analysis). Results: Medical schools use different policies and procedures to recruit rural and Indigenous students. Although longitudinal integrated clerkships offer many benefits, few students have access to them. Leadership representation on decision-making education committees differed across medical schools pointing to a variation in the value of rural physicians' perspectives. Conclusion: This study illustrated that medical schools are making efforts that align with the RRMA. It is critical they continue to make strategic decisions embedded in educational policy and leadership to reinforce the importance of and influence of rural medical education to support workforce planning.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".