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Record W4405128381 · doi:10.1111/pan.15053

Analysis of Transgender and Gender‐Diverse Topics Within Diversity, Equity, and Inclusion Curricular Content in Pediatric Anesthesiology Fellowship Programs in the United States and Canada—A Prospective Survey

2024· article· en· W4405128381 on OpenAlexaffabout
Marcus Krueger, Remigio A. Roque, Travis Reece‐Nguyen, Hilary MacCormick

Bibliographic record

VenuePediatric Anesthesia · 2024
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTransgenderInclusion (mineral)CurriculumMedicineGender diversityDiversity (politics)Medical educationAnesthesiologyFamily medicinePsychologyPolitical sciencePedagogyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Transgender and gender-diverse individuals experience higher burdens of health disparities compared to their cisgender counterparts. Contributing factors include decreased access and denial of care, experiences and fear of medical violence, and increasing legislative barriers. These patients often report having to educate healthcare professionals due to lack of expertise of transgender and gender-diverse issues within the medical community, with training deficits observed at all levels of medical training. AIMS: We surveyed pediatric anesthesiology fellowship directors in the United States and Canada with an aim to describe the current state of transgender and gender-diverse topics within diversity, equity, and inclusion curricular content across pediatric anesthesiology fellowship training programs. METHODS: A 25-question descriptive, cross-sectional, electronic survey was created and distributed to all pediatric anesthesiology fellowship program directors in the United States and Canada investigating the inclusion of transgender and gender-diverse topics within diversity, equity, and inclusion curriculum (if present) for pediatric anesthesiology fellows. RESULTS: A total of 33 responses were collected from 69 total invited programs. Diversity, equity, and inclusion curricula were present in 93.5% (29/31) of fellowship programs but only 29% (9/31) of programs included content specific to transgender and gender-diverse populations. Just 17% (5/29) of program directors thought their curriculum adequately prepared their graduates to care for transgender and gender-diverse patients. However, 69% (20/29) expressed a desire to see more educational content included in the future. Lack of knowledgeable faculty educators and time were the most chosen perceived barriers to inclusion. CONCLUSIONS: To our knowledge, this study is the first to evaluate pediatric anesthesiology fellowship curricula for inclusion of transgender and gender-diverse topics within diversity, equity, and inclusion curriculum. As the population of known transgender and gender-diverse youth increases, all pediatric anesthesiologists must be equipped to treat this population with appropriate clinical and cultural sensitivity. Future endeavors must include robust sexual orientation and gender identity data collection, access to educational materials from content experts, recruitment and empowerment of LGBTQ+ anesthesiologists, and increased dedicated time toward diversity, equity, and inclusion education, specifically in regard to the transgender and gender-diverse population. CLINICAL IMPLICATIONS: Lack of physician education on the care of transgender and gender-diverse patients is one of several factors contributing to the health disparities seen in this population; training deficits in the care of transgender and gender-diverse patients have been observed in all levels of medical training. Our data show a critical deficiency of transgender and gender-diverse topics within diversity, equity, and inclusion curricular content in pediatric anesthesiology fellowships in the United States and Canada and identify potential barriers to the inclusion of such content.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score0.717

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.110
GPT teacher head0.334
Teacher spread0.225 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2024
Admission routes2
Has abstractyes

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