Use of Free, Open Access Medical Education and Perceived Emergency Medicine Educational Needs Among Rural Physicians in Southwestern Ontario
Bibliographic record
Abstract
Free, open access medical education (FOAM) has the potential to revolutionize continuing medical education, particularly for rural physicians who practice emergency medicine (EM) as part of a generalist practice. However, there has been little study of rural physicians' educational needs since the advent of FOAM. We asked how rural physicians in Southwestern Ontario obtained their continuing EM education. We asked them to assess their perceived level of comfort in different areas of EM. To understand how FOAM resources might serve the rural EM community, we compared their responses with urban emergency physicians. Responses were collected via survey and interview. There was no significant difference between groups in reported use of FOAM resources. However, there was a significant difference between rural and urban physicians' perceived level of EM knowledge, with urban physicians reporting a higher degree of confidence for most knowledge categories, particularly those related to critical care and rare procedures. This study provides the first description of EM knowledge and FOAM resource utilization among rural physicians in Southwestern Ontario. It also highlights an area of educational need -- that is, critical care and rare procedures. Future work should address whether rural physicians are using FOAM specifically to improve their critical care and procedural knowledge. As well, because of the generalist nature of rural practice, future work should clarify whether there is an opportunity cost to rural physicians' knowledge of other clinical domains if they chose to focus more time on continuing education in critical care EM.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".