How early clinical experiences in rural communities influence student learning about rural generalism considered through the lens of educational theory
Bibliographic record
Abstract
Introduction: Rural communities have poorer health compared to urban populations due partly to having lesser healthcare access. Rural placements during medical education can equip students with the knowledge and skills to work in rural communities, and, it is hoped, increase the supply of rural physicians. It is unclear how students gain knowledge of rural generalism during placements, and how this can be understood in terms of place-based and/or sociocultural educational theories. To gain insight into these questions we considered the experiences of pre-clerkship medical students who completed two mandatory four-week rural placements during their second year of medical school. Methods: Data was collected using semi-structured interviews or focus groups, followed by thematic analysis of the interview transcripts. Results: Rural placements allowed students to learn about rural generalism such as breadth of practice, and boundary issues. This occurred mainly by students interacting with rural physician faculty, with the effectiveness of precepting being key to students acquiring knowledge and skills and reporting a positive regard for the placement experience. Discussion: Our data show the central role of generalist physician preceptors in how and what students learn while participating in rural placements. Sociocultural learning theory best explains student learning, while place-based education theory helps inform the curriculum. Effective training and preparation of preceptors is likely key to positive student placement experiences.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".