Exploring rural medical education: a study of Canadian key informants
Bibliographic record
Abstract
INTRODUCTION: Recruiting and retaining primary healthcare professionals is a global healthcare problem. Some countries have been using medical education as a strategy to aid in the recruitment and retention of these healthcare professionals. The purpose of this study is to engage with key informants and explore the learning processes that support medical students to prepare for a rural career. METHODS: Seven key informants with extensive experience in rural medical education participated in semi-structured interviews. The interviews were audio-recorded and professionally transcribed. Transcripts were analyzed using thematic analysis. RESULTS: Four key themes were identified. Respondents discussed the different ways they conceptualized 'rural'. Informants suggested that relationships could either be barriers or facilitators to rural practice and that certain educational strategies were necessary to help train students for rural careers. Finally, informants discussed different characteristics that rural physicians need. CONCLUSION: The finding of this study suggests that preparing students for rural practice requires a multifaceted approach. Specifically, using certain educational strategies, pre-selecting or developing certain characteristics in students, and helping students develop relationships that attach them to a community or support working in a rural community are warranted.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".