Rethinking Our Annual Congress—Meeting the Needs of Specialist Physicians by Partnering With Provincial Simulation Centers
Bibliographic record
Abstract
ABSTRACT: Canada's maintenance of certification programs for physicians has evolved to emphasize assessment activities. Our organization recognized the importance of offering more practice assessment opportunities to our members to enhance their practice and help them comply with a regulation from our provincial professional body related to ongoing continuing education. This led us to rethink our annual congress and enrich the program with a curriculum of interdisciplinary simulation sessions tailored to meet the needs of a broad audience of specialists. Our challenges are similar to those of many national specialty societies having limited access to simulation facilities, instructors, and simulation teams that can cover the breadth and scope of perceived and unperceived simulation needs for their specialty. Our innovative solution was to partner with local experts to develop 22 simulation sessions over the past three years. The response was very positive, drawing 867 participants. Over 95% of participants either agreed or strongly agreed that their simulation session (1) met their learning objectives, (2) was relevant for their practice, and (3) encouraged them to modify their practice. Narrative comments from a survey sent to the 2018 participants four months after their activity indicated several self-reported changes in their practice or patient outcomes. We were able to centralize offers from organizations that had previously worked in silo to develop simulation sessions meeting the needs of our members. Proposing simulation sessions allowed our organization to establish long-term partnerships and to expend our "educational toolbox" to address skill gaps not usually addressed during annual meetings.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".