Beyond bricks and mortar: a rural network approach to preclinical medical education
Bibliographic record
Abstract
BACKGROUND: Countries with expansive rural regions often experience an unequal distribution of physicians between rural and urban communities. A growing body of evidence suggests that the exposure to positive rural learning experiences has an influence on a physician's choice of practice location. Capitalizing on this observation, many medical schools have developed approaches that integrate rural exposure into their curricula during clerkship. It is postulated that a preclinical rural exposure may also be effective. However, to proceed further in development, accreditation requirements must be considered. In this investigation, academic equivalence between a preclinical rural community based teaching method and the established education model was assessed. METHOD: Two separate preclinical courses from the University of Calgary's three year Undergraduate Medical program were taught at two different rural sites in 2010 (11 students) and 2012 (12 students). The same academic content was delivered in the pilot sites as in the main teaching centre. To ensure consistency of teaching skills, faculty development was provided at each pilot site. Academic equivalence between the rural based learners and a matched cohort at the main University of Calgary site was determined using course examination scores, and the quality of the experience was evaluated through learner feedback. RESULTS: In both pilot courses there was no significant difference between examination scores of the rural distributed learners and the learners at the main University of Calgary site (p > 0.05). Feedback from the participating students demonstrated that the preceptors were very positively rated and, relative to the main site, the small group learning environment appeared to provide strengthened social support. CONCLUSION: These results suggest that community distributed education in pre-clerkship may offer academically equivalent training to existing traditional medical school curricula while also providing learners with positive rural social learning environments. The approach described may offer the potential to increase exposure to rural practice without the cost of constructing additional physical learning sites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".