Keeping family physicians in rural practice. Solutions favoured by rural physicians and family medicine residents.
Bibliographic record
Abstract
OBJECTIVE: To determine how family medicine residents and practising rural physicians rate possible solutions for recruiting and sustaining physicians in rural practice. DESIGN: Cross-sectional mailed survey. SETTING: Rural family practices and family medicine residency programs in Ontario. PARTICIPANTS: Two hundred seventy-six physicians and 210 residents. MAIN OUTCOME MEASURES Ratings of proposed solutions on a 4-point scale from "very unimportant" to "very important". RESULTS: Rural family physicians rated funding for learner-driven continuing medical education (CME) and limiting on-call duty to 1 night in 5 as the most important education and practice solutions, respectively. Residents rated an alternate payment plan to include time off for attending and teaching CME and comprehensive payment plans with a guaranteed income for locums as the most important education and practice solutions, respectively. CONCLUSION: Residents and physicians rated solutions very similarly. A comprehensive package of the highest-rated solutions could help recruit and sustain physicians in rural practice because the solutions were developed by experts on rural practice and rated by family medicine residents and practising rural physicians.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".