#GenderAffirmingHormoneTherapy and Health Information on TikTok: Thematic Content Analysis
Bibliographic record
Abstract
Background: Transgender and gender diverse people often turn to online platforms for information and support regarding gender-affirming hormone therapy (GAHT); however, analysis of this social media content remains scarce. Objective: We characterized GAHT-related videos on TikTok to highlight the implications relevant to GAHT prescribers. Methods: We used a web scraper to identify TikTok videos posted under the hashtags #genderaffirminghormonetherapy and #genderaffirminghormones as of November 2023. We identified recurrent themes via qualitative content analysis and assessed health education videos with the Patient Education Materials Assessment Tool for Audiovisual Materials (PEMAT-A/V) scale and a modified Currency, Relevance, Authority, Accuracy, and Purpose (CRAAP) test. Results: Out of 69 videos extracted, 71% (49/69) were created by GAHT users, 24.6% (17/69) were created by health care workers, and 21.7% (15/69) were created to provide health education. Themes included physical changes on testosterone, GAHT access, and combating misinformation and stigma surrounding GAHT. Health education videos scored highly on PEMAT-A/V items assessing understandability (mean 88.3%, SD 11.3%) and lower on actionability (mean 60.0%, SD 45.8%). On the CRAAP test, videos scored highly on the relevance, authority, and purpose domains but lower on the currency and accuracy domains. Conclusions: Discussions of GAHT on TikTok build community among transgender and gender diverse users, provide a platform for digital activism and resistance against legislation that limits GAHT access, and foster patient-provider dialogue. Educational videos are highly understandable and are created by reliable sources, but they vary in terms of currency and quality of supporting evidence, and they lack in actionability.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".