Gender Affirmation–Related Information-Seeking Behaviors in a Diverse Sample of Transgender and Gender-Diverse Young Adults: Survey Study
Bibliographic record
Abstract
BACKGROUND: Of the 1.6 million transgender and gender-diverse (TGD) people in the United States, approximately 700,000 are youth aged 13-24 years. Many factors make it difficult for TGD young people to identify resources for support and information related to gender identity and medical transition. These range from lack of knowledge to concerns about personal safety in the setting of increased antitransgender violence and legislative limitations on transgender rights. Web-based resources may be able to address some of the barriers to finding information and support, but youth may have difficulty finding relevant content or have concerns about the quality and content of information they find on the internet. OBJECTIVE: We aim to understand ways TGD young adults look for web-based information about gender and health. METHODS: In August 2022, 102 young adults completed a 1-time survey including closed- and open-ended responses. Individuals were recruited through the Prolific platform. Eligibility was restricted to people between the ages of 18-25 years who identified as transgender and were residents of the United States. The initial goal was to recruit 50 White individuals and 50 individuals who identified as Black, indigenous, or people of color. In total, 102 people were eventually enrolled. RESULTS: Young adults reported looking on the internet for information about a broad range of topics related to both medical- and social-gender affirmation. Most participants preferred to obtain information via personal stories. Participants expressed a strong preference for obtaining information from other trans people. CONCLUSIONS: There is a need for accessible, expert-informed information for TGD youth, particularly more information generated for the transgender community by members of the community.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".