68 Are Health Equity Rounds (HER) an acceptable format to address education on implicit bias and structural racism in pediatric emergency settings?
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,038 | 0,092 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».