Identity-Based Bullying Victimization Among Canadian Adolescents: Experiences of Transgender and Gender Diverse Youth
Bibliographic record
Abstract
Identity-based bullying, also known as bias-based and stigma-based bullying, is bullying that occurs due to a real or perceived social identity. Youth from equity-deserving communities, such as transgender and gender diverse youth, are more likely to experience both general bullying victimization and identity-based bullying victimization. The current study used nationally representative Canadian data from the 2022 Health Behaviours in School-Aged Children (HBSC) study to examine (a) the prevalence of different forms of bullying victimization, including identity-based bullying, among students of diverse gender identities in two grade cohorts (grades 6–8 and grades 9–10); and (b) the association between these victimization experiences and psychological complaints. Participants consisted of 26,571 youth in grades 6 to 10 from across Canada, including transgender girls ( n = 108), transgender boys ( n = 298), and gender diverse youth ( n = 1,169) who completed self-report measures in school. Factor analyses demonstrated that general and sex/gender identity-based victimization were unique. In general, transgender and gender diverse youth experienced higher levels of both types of victimization relative to their cisgender peers. Multigroup structural equation modeling indicated that sex/gender identity-based bullying was positively associated with psychological complaints for transgender and gender diverse youth only. Findings suggest that approximately one in two transgender and gender diverse youth experience bullying victimization regularly. This victimization is highly pervasive, tends to target their gender and/or sexual orientation, and is associated with poor mental health. Results underscore the importance of including specific components on identity-based bullying, bias, prejudice, and discrimination in bullying prevention and intervention efforts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".