Are Health Equity Rounds an Acceptable Format to Address Education on Implicit Bias and Structural Racism in Pediatric Emergency Settings?
Bibliographic record
Abstract
OBJECTIVES: Implicit bias and structural racism in pediatric health care cause significant inequities and poorer outcomes. To help educate pediatric health-care providers, health equity rounds (HER) engage teams in discussions on the impact of bias and racism on patient care using a case-based curriculum. This mixed-methods study assessed the feasibility and acceptability of incorporating HER in a pediatric emergency medicine (PEM) setting. METHODS: Two HERs, focusing on implicit bias in medicine (June 2021) and linguistic barriers to health care (December 2021), were completed during the institution's multidisciplinary, PEM update rounds. Cases presented were selected if patient care and/or outcomes were negatively affected, were appropriate for educational discussion and relevant to the presentation topic. HER participants were invited to complete an online survey and semi-structured interview post-HER to explore their experiences, including professional and personal impacts. RESULTS: Both HERs were well-attended and had a moderate survey uptake (20/25 vs. 14/22). Three-quarters of survey participants found HER engaging (80%), and believed the learned objectives would impact their clinical practice (73.7% vs. 78.6%). Responses varied between presentations for educational value (80% vs. 61.6%) and interest in future HERs (94.7% vs. 78.6%). Four themes emerged from 3 qualitative interviews: HER satisfaction and experience, influence on service provision, supports and resources, and ideas for future HERs. CONCLUSIONS: Our findings suggest HER is an acceptable and feasible forum for discussing and reflective practice on relevant topics in PEM educational sessions. Implementing HER in other specialized areas or settings and the impact of different topics should be explored further.
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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 | 0.041 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".