68 Are Health Equity Rounds (HER) an acceptable format to address education on implicit bias and structural racism in pediatric emergency settings?
Bibliographic record
Abstract
Abstract Background Implicit bias and structural racism in healthcare create significant social and health inequities that can lead to poorer care and health outcomes. Canadian medical training programs have created curricula addressing these issues, but there remains a lack of education targeting faculty members and multidisciplinary teams. Perdomo et al. created a longitudinal case-based curriculum called Health Equity Rounds (HER) to engage faculty and teams in discussions about the effects of implicit bias and racism on patient care. Curriculum pilot results suggested some opportunities to mitigate the potential impact on patient care. Objectives This study assessed the feasibility, acceptability, and effectiveness of incorporating HER for multidisciplinary educational rounds in a Pediatric Emergency Medicine (PEM) division. By following previously published curricula, our HER aimed to provide a forum to discuss evidence-based strategies to address implicit bias and structural racism in healthcare. Design/Methods Two HER were embedded within PEM Update Rounds over six months and targeted an interdisciplinary audience from all stages of training. Selected cases were provided by the institution’s Equity, Diversity, and Inclusivity steering committee, where 1) patient care and/or outcomes were affected by implicit bias and/or racism, and 2) experiences were appropriate for analysis and education. Participants in this mixed-methods study completed an online survey following HER and were invited to individual interviews that further explored their experience and any resulting professional and personal impacts. Results The two topics selected for HER rounds were Implicit Bias in Medicine and Linguistic Barriers to Healthcare, with high post-rounds survey uptake of 80% (20/25) and 65% (14/22), respectively. Among respondents, 73.7% and 78.6% indicated that learned objectives would impact their clinical practice; 80% found both presentations engaging; 80% and 61.6% found educational value of HER to be good/excellent; and 94.7% and 78.6% indicated interest in future HER presentations. Three respondents completed interviews. After thematic analysis, overarching themes included receptiveness to creating more equitable infrastructures in PEM; equity being a shared and multi-disciplinary responsibility; and strategies for implementing HER topics into practice. Conclusion There is a need in academic and clinical medicine to address implicit bias and structural racism as contributors to health inequities. Positive feedback from this study suggests HER may be an acceptable and feasible forum to promote safe discussion and reflective practice on potentially provoking topics within interdisciplinary PEM educational sessions.
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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 | 0.038 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".