Medical Information Provided by Transgender and Gender-Diverse Content Creators on YouTube: Descriptive Content Analysis
Bibliographic record
Abstract
Background: Transgender and gender-diverse (TGD) individuals frequently turn to social media to find community, express their identities, and access essential information. These platforms are easily accessible to TGD people and enable health information-seeking in anonymous, identity-affirming spaces outside of traditional health care systems. As a result, social media has become a critical source of health information on topics like gender-affirming care for TGD individuals, specifically for TGD youth. YouTube, one of the most widely used social media platforms, is especially popular for its long-form videos made by content creators who have built dedicated followings on the platform. Among them are TGD content creators, many of whom make content documenting their medical transition and gender identity journey and provide general information about TGD topics. TGD creator content therefore makes YouTube an important platform for health education for TGD individuals. Objective: This study aims to describe the health-related content shared by TGD content creators on YouTube. Specifically, we characterize the medical topics addressed, the frameworks used to discuss these topics, and the valence of creators' health care experiences. Methods: A descriptive content analysis was performed on 2485 videos posted by 42 self-identified TGD YouTube content creators. Videos were systematically evaluated for mentions of gender-affirming care and other health-related topics. We also examined whether creators framed medical information using personal narratives or an informational approach and if they characterized their medical experiences as positive, negative, or neutral. Results: Most videos (n=1724, 69.4%) created by TGD content creators did not include discussions related to gender identity or transitioning. However, among the videos that did address gender identity (n=761, 30.6%), mentions of medical topics were prevalent (n=554, 72.8%). Of videos that discussed medical topics, gender-affirming surgeries (n=356, 64.3%) and hormone replacement therapy (n=307, 55.4%) were the most frequently discussed. Other commonly discussed medical topics included mental health (n=131, 23.6%) and sexual health (n=96, 17.3%). Videos covering medical topics primarily centered on personal experiences (n=411, 74.2%), with content creators often characterizing these experiences positively (n=224, 73.2%). Conclusions: This study highlights the breadth of health-related information shared by TGD content creators on YouTube. Our findings underscore the role of long-form video content on YouTube as an educational resource for TGD people, offering health information that is both easy to access and grounded in lived experience. Clinicians can use these findings to better understand the health information that their TGD clients are likely to encounter online, fostering more informed and supportive conversations about gender-affirming care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".