Survey Evaluation of the Role of Social Media and Social Support for Transgender, Nonbinary, and Intersex People: Observational Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».