Exploring the Impact of Bullying and Friendship on Mental Health Outcomes Among Transgender, Gender Diverse (TGD), and Cisgender Lesbian, Gay, Bisexual (LGB+) Youth
Bibliographic record
Abstract
Prejudice and discrimination put 2SLGBTQ+ youth at greater risk of developing poor mental health outcomes. Yet there is heterogeneity within this group, notably a greater risk for poor mental health outcomes in trans and gender diverse (TGD) youth. The goal of this study was to determine if the relationship between bullying and mental health outcomes, and the potential protective influence of peer friendship, differed between cisgender lesbian, gay and bisexual (LGB+) and TGD youth. Using data from the 12-year youth survey from All Our Families cohort (n = 221), we analyzed youth’s responses on Behavior Assessment System for Children (depression and anxiety), Middle Years Development Instrument (happiness and friendship) and self-reports on bullying within the last year. Bullying was positively associated with anxiety and depression for TGD youth, and for the cisgender LGB+ group, bullying was associated with anxiety symptoms. Friendship buffered the association between bullying and anxiety symptoms for TGD youth. Our findings highlight the importance of examining LGB+ and TGD youth independently, given the heterogeneity found within the broad 2SLGBTQ+ group, as demonstrated by diverse mental health and bullying outcomes. It supports the potential positive influence of friendship on anxiety symptoms, particularly for TGD youth in early adolescence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".