The role, the risk, and the reciprocity: creating positive early rural placements in medical education
Bibliographic record
Abstract
INTRODUCTION: The Northern Ontario School of Medicine University seeks to address rural physician shortages in Northern Ontario. One key strategy the school employs is the use of experiential learning placements embedded throughout its undergraduate curriculum. In second year, students embark on two 4-week placements in rural and remote communities. This study sought to explore the factors that contribute to a positive learning experience from the preceptor's perspective. METHODS: Semi-structured interviews were conducted with five community preceptors who have participated in these placements. Using the information from these interviews a survey was created and sent to another 15 preceptors. Data were analyzed using qualitative methods and frequencies. RESULTS: Three key themes were identified from both the interviews and survey data: the role of early rural and remote placements; the risks of these placements; and the need for a reciprocal relationship between institutions, preceptors, and students to create a positive learning environment. CONCLUSION: Preceptors value the opportunity to teach students, but the aims of these placements are not clear and preceptors and local hospitals need more workforce resources to make these experiences positive.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".