A Qualitative Investigation of the Experiences of Students and Preceptors Taking Part in Remote and Rural Community Experiential Placements During Early Medical Training
Bibliographic record
Abstract
BACKGROUND: Medical education can help alleviate the chronic undersupply of physicians to rural communities. Providing students with early rural clinical experiences may allow the gaining of necessary knowledge and skills to practice and live rurally, as well as the desire to do so. PURPOSE: This study aims to provide a detailed understanding of Remote and Rural Community Placements (RRCPs) which occur in the second year of a Doctor of Medicine programme. METHODOLOGY/APPROACH: Using a thematic analysis approach, we examined the experiences of students and preceptors in the RRCP. Data were collected using semi-structured interviews and focus groups. FINDINGS/CONCLUSIONS: Students valued RRCPs as a formative clinical experience and preceptors gained professionally from participating. The RRCPs enhanced students regard for, and knowledge of, rural medicine. Yet, contrary to the stated aims of the placement, students spent very little time in activities outside of the clinic, neither learning about the community nor about the life of a physician as a community member. IMPLICATIONS: Medical educators should recognise that students and preceptors will inevitably place different value on the different sociocultural and perceptual aspects of placements, namely clinical and non-clinical. As such, the curriculum should draw clearly articulated links between each.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".