Rural medical education as a solution to rural physician shortages: an exploration of the motivations and engagement of rural physician-educators in Prince Edward Island, Canada
Bibliographic record
Abstract
INTRODUCTION: Medical education is increasingly taking place in rural areas as this is known to help physician recruitment to rural areas. A medical school is planned for Prince Edward Island (PEI) that would use community-based learning as a core principle, yet little is known about the specific factors that influence our rural physicians' participation and engagement in medical education. Our objective is to describe these factors. METHODS: Using mixed-methods, we conducted a survey of all physician-teachers on PEI and conducted semi-structured interviews with self-selected survey respondents. We gathered quantitative and qualitative data, and conducted an analysis of themes. RESULTS: The study is ongoing and will be completed before March 2022. Early survey results suggest that faculty teach because they enjoy it, and due to a sense of \"paying it forward\" and \"duty\". They face major workload challenges but are very interested in improving their teaching skills. They see themselves as clinician-teachers but not as scholars. DISCUSSION: Locating medical education in rural communities is known to alleviate physician shortages in those areas. Our early findings suggest novel factors such as identity, and traditional factors such as workload and resources, influence teaching engagement for rural physicians. Our findings also suggest that rural physicians' interest in improving their teaching is not being met by current methods. Our research contributes to the study of factors influencing rural physicians' motivation and engagement in teaching. Further research is required to understand how these findings compare with urban settings, and the implications of these differences for supporting rural medical education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".