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Record W4307494179 · doi:10.1097/acm.0000000000004873

Perspectives of Transgender and Nonbinary Health Care Providers on Gender Minority Patient Simulation

2022· article· en· W4307494179 on OpenAlexaboutno aff
Emily J. Noonan, Ryan Combs, Carrie Bohnert, Hallie Decker, Caison Black, L Weingärtner

Bibliographic record

VenueAcademic Medicine · 2022
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsTransgenderThematic analysisSituatedHealth carePsychologyQualitative researchMedical educationBest practiceNursingSocial psychologyMedicineSociologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.021
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0140.009
Scholarly communication0.0060.004
Open science0.0010.011
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.071
GPT teacher head0.422
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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