Remote and rural placements occurring during early medical training as a multidimensional place-based medical education experience
Bibliographic record
Abstract
The Northern Ontario School of Medicine delivers medical education aiming to improve the health outcomes for persons living in Northern Ontario, including those in underserviced rural and geographically remote communities. Second year students experience rural medicine and living during two four-week long placements set in remote and rural communities (RRCP) supervised by local physicians. This place-based approach to medical education aims to equip learners with the skills and dispositions needed to work there successfully. The goal of the study was to develop a better understanding of RRCPs from different perspectives: Institutional, community-preceptors and students. Data was collected by review of institutional documents, semi-structured interviews, and questionnaires to obtain information about the aims of each group. A place-based educational framework informed the analysis which developed themes and sub-themes using a constructivist approach. The aims of each group were in five themes, social accountability, community engagement, integrated learning, forming the rural clinician, and living in place as a rural clinician. Differences were, however, apparent in terms of emphasis and perceived relevance, with these being related to the perceptual, political, ideological and social dimensions. For example, the finding that students did not value extra-clinical learning about or within the wider community can be viewed as students having a different place-relationship with the community than their teachers in terms of the social dimension. The data suggests that curricula should include consideration of the various ways students and teachers interact with placement communities with the aim of gaining understanding of, and bridging the gap between, their different expectations. Key words: Place-based education, medical education, rural placements.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".