Exploring Health Care Professionals’ Perspectives on Education, Awareness, and Preferences for Digital Educational Resources to Support Transgender, Nonbinary, and Intersex Care: Interview Study
Bibliographic record
Abstract
BACKGROUND: Health care professionals often face challenges in providing affirming and culturally competent care to transgender, nonbinary, and intersex (TNBI) patients due to a lack of understanding and training in TNBI health care. This gap highlights the opportunity for tailored educational resources to enhance health care professionals' interactions with TNBI individuals. OBJECTIVE: This study aimed to explore health care professionals' perspectives on education and awareness of health issues related to TNBI individuals. Specifically, it aimed to identify their needs, challenges, and preferences in accessing and using digital educational resources to enhance their knowledge and competence in providing inclusive and effective care for this population. METHODS: A qualitative research approach was used in this study. In total, 15 health care professionals were recruited via convenience sampling to participate in semistructured interviews. Thematic analysis was applied to identify recurring codes and themes. RESULTS: The study identified several themes and subthemes related to gender diversity awareness, inclusive communication and understanding the needs of TNBI individuals, societal and structural challenges, regulatory gaps in training and support infrastructure, education and training needs for health care professionals on TNBI care, educational resources and training tools for TNBI care, challenges and design considerations for eHealth tools integrations, and evaluating eHealth impact. Participants identified communication barriers, the need for health care providers to use inclusive language, and gaps in both health care system infrastructure and specialized training for gender-affirming care. In addition, participants expressed a need for comprehensive education on transgender and nonbinary health issues, resources for mental health professionals, user-friendly design, and accessibility features in eHealth tools. CONCLUSIONS: The study revealed substantial deficiencies in health care professionals' knowledge of gender diversity, cultural competency, and the importance of inclusive communication. Addressing the identified barriers and challenges through targeted interventions, such as providing training and support for health care professionals, investing in user-friendly design and data security, and promoting cultural competence in TNBI health care, is essential. Despite integration challenges, eHealth tools have the potential to improve patient-health care professional relationships and access to care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".