62. Does an expert presentation raise awareness of CanMEDs Roles among residents?s
Bibliographic record
Abstract
We set out to determine whether an expert presentation on CanMEDS would raise awareness of CanMEDS roles among residents. We addressed this question with paired surveys distributed before and after the expert presentation. Each survey outlined seven different clinical scenarios each of which required one of the seven core CanMEDS competencies. Paired surveys were distributed prior to the presentation to the audience that was composed of a selection of residents from various disciplines. One survey was filled out prior to and the second survey completed following the expert presentation. Data were analysed using nonparamentric statistical methods. There was in general, a low pre-presentation background knowledge of CanMEDS roles, with wide variability between specialties. Our hypothesis that disciplines with less patient contact would have less understanding of CanMEDS roles was not fully supported. All specialties demonstrated improvement in their understanding of CanMEDS roles in the post-presentation survey. While there is a low background level of knowledge about CanMEDS roles, we determined that following an expert presentation (in this case by Dr. Serita Verma) the residents were significantly more able to correctly apply the core competencies of the CanMEDS model to the given clinical scenarios. We propose that an expert presentation could be applied as an innovative educational tool advancing CanMEDS education among residents.
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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 | 0.015 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".