International Congress on Academic Medicine: 2024 medical education abstracts
Bibliographic record
Abstract
Background/Purpose: Learners from marginalized backgrounds are disproportionally represented in experiences of discrimination.Equity Diversity and Inclusion (EDI) curriculum development studies show that simulation sessions, improve readiness to respond to discriminatory comments in the workplace.A workshop was developed to lay the foundation for an EDI training curriculum in residency programs.Purpose: To assess the understanding of EDI-related topics in Canadian Residency Training Programs and to determine the efficacy of videobased simulation in knowledge acquisition pertaining to EDI topics.Methods: Participants were Pediatric and Internal medicine residents at Memorial University of Newfoundland and the University of Calgary.Participants completed a pre-intervention survey to assess baseline knowledge.Participants watched videos developed by the University of Calgary on racism and gender inequity, followed by a facilitated discussion of the videos.They completed a post-intervention survey to assess level of knowledge gain.Results: Most residents, 81%, felt the workshop increased the chance of responding to a microaggression that occurs to someone else in the workplace.Ninety six percent felt the workshop had given them tools on how to intervene in the case of a microaggression and 85% felt like they were probably and likely to use the tools received in the workshop in the future. Discussion:Implementing a workshop on microaggressions into curricula should be a fundamental part of residency training programs.By creating workshops, residents can feel empowered to identify and respond to microaggressions in the workplace.This can help create a training environment that supports diversity and inclusion, thereby contributing to wellness and work satisfaction OC-1-2 Experiences and Perspectives of Racism Among Trainees in Canadian Postgraduate Training Programs
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.021 | 0.025 |
| Insufficient payload (model declined to judge) | 0.047 | 0.042 |
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".