Survey Evaluation of the Role of Social Media and Social Support for Transgender, Nonbinary, and Intersex People: Observational Study
Bibliographic record
Abstract
Background: Transgender and gender-diverse (TGD) people experience greater health disparities than their cisgender counterparts. Social determinants of health are linked to these health disparities in minority communities, including the TGD community. Lack of social support contributes significantly to these disparities for the TGD community. Objective: The aim of this study was to evaluate the role of social media and social support groups among TGD patients who attend a transgender clinic. Methods: A questionnaire was developed through an iterative process and emailed to TGD people attending a tertiary care TGD-focused clinic. The survey assessed social media use (platforms, duration, and adverse effects), social support groups (past participation and interest in current participation), and demographic characteristics (age, gender, race and ethnicity, educational level, religious affiliation, and income). Results: Our survey garnered 48 responses. Of these participants, 50% (n=24) identified as transfeminine or transgender women, 29.2% (n=14) identified as transmasculine or transgender men, 8.3% (n=4) identified as nonbinary, 2.1% (n=1) identified as genderfluid, and 10.4% (n=5) identified as another identity. Our respondents' average age was 35 (SD 15.6) years. Nearly 70% (n=31, 64.6%) reported at least monthly transphobia, and 35.4% (n=17) reported at least weekly transphobia. Primary social support was reported as coming from an in-person significant other or friend 49% (n=24) of the time and from social media or online friends 12.5% (n=6) of the time. Social media was used for the primary purpose of interacting with queer or TGD people by 65% (n=33) of respondents, and the most common sites used were Discord, Reddit, and Instagram. Among respondents who either were attending or had attended a gender identity-focused support group, 61% (14/23) reported them being beneficial. In total, 52% (25/48) had never attended a support group related to their gender identity, and 60% (15/23) were open to attending. Conclusions: This study found that social media is already being used by TGD people for the purpose of interacting with other queer and transgender people but also that there are risks associated with its use. Given this reality, counseling patients on social media use should focus on safety in use and honest discussions of both the risks and benefits associated with its use. Regarding social support groups focused on gender identity, many current or previous attendants reported that support groups were helpful for finding social support, especially early on in one's transition and when other avenues of support are not present. Additionally, many respondents who had never attended a support group were interested in attending for the perceived benefits of increased social support and interest in meeting other community members. Engaging TGD patients in the use of social media and social support groups for gender identity may help improve support, although exposure to hate and transphobia is a risk that comes with social media use.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".