Perspectives of Transgender and Nonbinary Health Care Providers on Gender Minority Patient Simulation
Notice bibliographique
Résumé
Health disparities experienced by gender minority (GM; i.e., transgender or nonbinary) patients have become a recent focus in health professions education. 1 However, trainee competencies, guidelines, and best practices have not been established or widely discussed. A 2021 study of patient simulation professionals from across the United States and Canada found a lack of consensus about who should portray GM patients. 2 Also, a 2020 study of GM standardized patients (SPs) presented important insight into casting considerations. 3 Both studies highlight the need for the field of medical education to consider best practices that support authenticity and ethics in simulation. To establish best practices for GM patient simulation, this study sought to understand the perceptions of GM health care providers, who are uniquely situated by virtue of understanding the experience of being a GM and having received health care training via patient simulation. In 2020, we began recruiting participants who were health care providers and identify as GMs through social media and the authors’ networks. Qualitative interviews were conducted in 2020–2021 (n = 22). Data were analyzed using thematic discursive analysis, 4 meaning the team analyzed both the themes that emerged from the data and the verbalized processes interviewees went through to express their beliefs about GMs in patient simulation. Most participants demonstrated an ideological consensus around the question of who should portray GM patients in SP simulation encounters. In response to 3 scenarios presented to participants regarding the social appropriateness, institutional feasibility, and implications of whether cisgender individuals could portray GM patients in simulation education, the majority of participants (n = 22) expressed that they would prefer that only GM individuals be offered employment as GM SPs. However, participants thought deeply and wrestled with different ideas as they were presented with the scenarios, with some accepting that cisgender SPs could portray GMs with robust training, and in the absence of sufficient GM SPs. The question of who should portray GM patient experiences in SP education was nuanced, multidirectional, and oftentimes, contradictory. GM inclusion in patient simulation was perceived to have an impact on learners, SPs themselves, GM communities, and the larger health care system. Authentic portrayal can help health care professionals deliver more respectful and effective gender-affirming care. The results of this study may guide the development of best practices in health professions education that are informed by the lived experience and health care expertise of GM providers. Currently, many simulation programs cast GM patients with cisgender SPs. The outcomes of our study with GM providers—who are likely to be the most understanding of the restrictions of patient simulation in academic settings—suggest that there are inherent, negative reactions to casting cisgender SPs in these roles. Thus, programs should engage the community in this work to build trust and avoid stereotyping and other harmful practices and should work to reflect the diversity of GM experiences in both case content and patient portrayal.
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 | 0,017 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,014 | 0,009 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,001 | 0,011 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,000 |
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 ».