A comparison of teaching opportunities for rural and urban family medicine residents
Bibliographic record
Abstract
CONTEXT: Medical schools of geographically large nations have expanded into rural areas to facilitate the development of a sustainable rural pipeline of physicians. Preceptor, or clinical teacher, recruitment at these sites has been an ongoing challenge. However, residents-as-teachers (RaT) curricula have not been modified to support the development of rural teachers. This study aimed to compare teaching opportunities between rural and urban family medicine residents and to identify mechanisms underlying potential differences. METHODS: Year-1 and Year-2 family medicine residents at seven Canadian institutions participated in a mixed-methods study utilising a quantitative survey and a qualitative interview. Rural and urban residents rated the quantity and types of teaching opportunities available during their training, from which a chi-squared analysis was completed. Volunteer respondents participated in a structured interview, from which a thematic analysis was performed. RESULTS: (4, n = 242) = 45.26, P < .000, Bonferroni's adjusted P < .000. Thematic analysis centred around determining factors influencing teaching opportunities and identified that the academic context, personal factors and programme factors were key dimensions. Within these dimensions, the number of medical students, a desire to be an educator and administrative support were cited as influences on teaching opportunities. CONCLUSIONS: The lack of teaching opportunities for rural trainees is attributable to a combination of practical and organisational factors revealed through thematic analysis. If rural graduates are not comfortable balancing the demands of service and teaching, this could compound the already prevalent issue of rural preceptor recruitment. It is essential to develop a rural-focused RaT curriculum to close this gap and produce competent educators who are ready to inspire generations of 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".