Rural medicine interest groups at McMaster University: a pilot study.
Bibliographic record
Abstract
INTRODUCTION: Although rural medicine interest groups (RMIGs) are prevalent in Canadian medical schools, there is little research on their contribution to rural education, training and careers. METHODS: We explored 2 broad questions by means of an electronic survey to people who were RMIG participants at McMaster University from 2002 to 2007: 1) What are the experiences of undergraduate trainees in an RMIG? 2) What are the features of RMIGs that contribute to an interest in rural medicine? The survey itself contained 35 questions broken down into sections detailing demographics, involvement in RMIGs, RMIG features, core and elective experiences, careers and Canadian Resident Matching Service. RESULTS: Of the 63 participants who completed the survey, 13 (20.6%) were in postgraduate training and 50 (79.4%) were in undergraduate training. The mean (standard deviation) age of participants was 28.4 (6.5) years and 71.9% percent were female. Respondents indicated that rural placements had the most influence on their choice of specialty and rural interest. Of all the features and activities of the RMIG, rural medicine special events contributed the most to an interest in rural medicine (e.g., "rural medicine days"). CONCLUSION: At McMaster University, the responses of participants suggested that RMIG participation had more influence on career choice than did the medical school attended. Communities, government organizations, residency programs and others interested in improving access to rural physicians, will note the importance of RMIGs and the importance survey respondents gave to rural medicine special events and rural electives.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".