Where do rural family medicine residents in Canada train?
Bibliographic record
Abstract
OBJECTIVE: To report on contextual variance in the distributed rural family medicine residency programs of 3 Canadian medical schools. DESIGN: A constructivist grounded theory methodology was employed. SETTING: Rural and remote postgraduate family medicine programs at the University of Alberta, the University of British Columbia, and the University of Calgary. PARTICIPANTS: Twenty-six family practice residents were interviewed, providing descriptions of 27 different rural sites and 10 regional sites. METHODS: Interviews were audiorecorded, transcribed verbatim, and thematically analyzed. MAIN FINDINGS: Participants differentiated between main campus academic health science centres; regional referral hub sites; and smaller, rural, and more remote community sites. Participants described major differences between sites in terms of patient, practice, educational, physical, institutional, and social factors. The differences between training sites included variations in learning opportunities; physical challenges related to weather, distance, and travel; and the social opportunities offered. There were also differences in how residents perceived their training sites, both in terms of what they noticed and how they interpreted their observations and experiences. Although there were contextual differences between regional sites, those differences were a lot less than between different smaller rural and remote sites. These differences shaped the learning opportunities available to residents and influenced their well-being. CONCLUSION: Although there may be some similarities between distributed training sites, each training context presents unique challenges and opportunities for the family medicine residents placed there. More attention to the specific affordances of different training contexts is required.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".