Going the distance: early results of a distributed medical education initiative for Royal College residencies in Canada
Bibliographic record
Abstract
BACKGROUND: There is a shortage of specialty physicians practising in rural Canada: only 2.4% of Canadian specialist physicians practise rurally. Numerous strategies have been proposed and attempted that aim to increase the number of rural physicians. These include undergraduate and postgraduate distributed medical education opportunities. The Distributed Royal College Initiative at the University of Calgary is increasing the exposure of specialty residents to rural medicine through regional rotations and electives. An assessment of the initial impacts of this programme was made. METHODS: Specialty residents were sent a voluntary survey following their regional rotation in academic year 2010-2011. The survey measured each resident's satisfaction with the experience, interest in undertaking another rotation and the impact of the rotation on potential rural practice location. The survey asked for written comments on the rotation. Data were analysed using descriptive statistics. RESULTS: A total of 73% (29) of the 40 eligible residents completed the survey that was distributed upon completion of the rotation. In the survey, 45% of respondents indicated they would have been likely to practise in a regional community prior to the experience. This changed to 76% following the rotation. Analysis of the comments revealed strong positive characteristics of the experience across all disciplines. CONCLUSIONS: Specialty-based, rural distributed programmes were perceived by the residents as educationally valuable and may be crucial in helping shift attitudes towards rural practice. Specific successful characteristics of the rotations provide direction to increase their quality further. These findings need to be verified in a larger sample.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".