Canadian rural family medicine training programs: growth and variation in recruitment.
Bibliographic record
Abstract
OBJECTIVE: To document the proliferation of rural family medicine residency programs and to note differences in design as they affect rural recruitment. DESIGN: Descriptive study using semistructured telephone interviews. SETTING: All family medicine residency programs in Canada in 2002. PARTICIPANTS: Directors of Canadian family medicine residency programs. MAIN OUTCOME MEASURES: Number of rural training programs and positions; months of rural exposure, degree of remoteness, and specialist support of rural communities within rural training programs. RESULTS: The number of rural training programs rose from one in 1973 to 12 in 2002. Most medical schools now offer dedicated rural training streams. From 1989 to 2002, the number of rural residency positions quadrupled from 36 to 144; large jumps in capacity occurred from 1989 to 1991 and then from 1999 to 2001. Rural positions now represent 20% of all family medicine residency positions. Among rural programs, minimum rural exposure ranged from 4 to 12 months, and the median distance between rural training communities and referral sites ranged from 50 to 440 km (median 187 km). Rotations in communities with no hospital were mandatory in five of 12 rural programs, optional in five, and unavailable in two. The proportion of training communities used by rural programs that had family physicians only (ie, no immediate specialty backup) ranged from 0 to 78% (mean 44%). Most training communities (78%) used by rural programs had fewer than 10 000 residents. Four of 12 rural programs offered various specialty medicine rotations in small communities. CONCLUSION: Rural residency programs have proliferated in Canada. The percentage of residency positions that are rural now equals the proportion of the general population in Canada living in rural areas. National guidelines for rural programs recommend at least 6 months of rural rotations and at least some training in communities without hospitals. Major variations among programs exist, and most program designs differ from designs recommended in national guidelines in at least one aspect.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".