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Social Support and Mental Health Among Transgender and Nonbinary Youth in Quebec.

Kira London-NadeauNicholas ChadiAshley B TaylorAce ChanAnnie Pullen SansfaçonLyne ChiniaraClaire LefebvreElizabeth M Saewyc
Published in: LGBT health (2023)
Purpose: Transgender and nonbinary (TNB) youth face elevated levels of discrimination, stigma, mental health disorders, and suicidality when compared with their cisgender counterparts. Family and school support may mitigate some of the effects of the stressors facing TNB youth. This study aimed to better understand the impact of each of these sources of support on TNB youths' mental health and wellbeing. Methods: We used data collected between 2018 and 2019 as part of the Canadian Trans Youth Health Survey, a bilingual online survey to measure social support, physical health, and mental health in a sample of 220 TNB youth aged 14-25 living in Québec, Canada. We examined the relationships among different sources of support, and mental health and wellbeing outcomes using logistic regression. Analyses were conducted on the full sample and according to linguistic groups (French and English). Results: Participants reported high levels of mental health symptoms, self-harm, and suicidality, and mental health symptoms were higher in the English-speaking group ( p  = 0.005). In models controlling for age, family connectedness was associated with good/excellent self-reported mental health (odds ratio [OR] = 2.62, p  = 0.001) and lower odds of having considered suicide (OR = 0.49, p  = 0.003) or attempted suicide (OR = 0.43, p  = 0.002), whereas school connectedness was associated with higher odds of good/very good/excellent general (OR = 2.42, p  = 0.013) and good/excellent mental (OR = 2.45, p  = 0.045) health. Conclusion: Family and school support present consistent associations with TNB youths' health and may constitute key areas for intervention for those supporting them.
Keyphrases
  • mental health
  • social support
  • mental illness
  • depressive symptoms
  • healthcare
  • public health
  • metabolic syndrome
  • adipose tissue
  • climate change
  • deep learning
  • hiv infected
  • artificial intelligence