Medical Information Provided by Transgender and Gender-Diverse Content Creators on YouTube: Descriptive Content Analysis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,003 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,008 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».