Cited by 28Bullying, Victimization, and AggressionLGBTQ Health, Identity, and PolicyGender, Sexuality, and Education

Associations of nonconforming gender expression and gender identity with bullying victimization: an analysis of the 2017 youth risk behavior survey

Open access
Lian, Qiguo, Li, Ruili, Liu, Zhihao, Li, Xiaona, Su, Qiru, Zheng, Dongpeng·2022·BMC Public Health

Abstract

Purpose: Although gender nonconformity (GNC) and transgender identity are both linked to bullying victimization, few studies have examined them with bullying victimization simultaneously. Using a sample of Youth Risk Behavior Survey, we investigated the associations of GNC and transgender identity with bullying victimization within the same study. Methods: We analyzed data from the cross-sectional school-based Youth Risk Behavior Survey in 2017 ( n = 25,378). The exposures were GNC and transgender identity. The main outcomes were traditional victimization, cyber victimization, and combined victimization. We calculated adjusted prevalence ratios (APRs) with 95% confidence intervals (CIs) using Poisson regression models. Results: There were 22.15% of high school students with GNC, and 1.61% identified themselves as transgender. GNC is associated with traditional (APR,1.45;95%CI, 1.21–1.73), cyber (APR,2.00; 95%CI, 1.66–2.40) and combined victimization (APR,1.61;95%CI, 1.42–1.83) respectively among assigned male at birth (AMAB) students only. Transgender male and female students are both at higher risk of bullying victimization for all the three outcomes than cisgender peers. Conclusions: AMAB GNC and transgender identity are associated with a higher risk of bullying victimization. Providing support systems and celebrating gender diversity within and outside schools are important.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s12889-022-13071-6

Cites

34 of 34 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Most cyberbullying research that includes TGD individuals is focused on LGBT adolescents rather than on the wider LGBTQIA+ community or TGD individuals specifically.

    “Most cyberbullying research that includes TGD individuals is focused on LGBT adolescents rather than on the wider LGBTQIA+ community or TGD individuals specifically [3,22,23,24].”

    From A Phenomenological Investigation into Cyberbullying as Experienced by People Identifying as Transgender or Gender Diverse (2022) · cites this paper as [23]

    The Background (page unavailable) says many studies group LGB and transgender adolescents under the LGBT umbrella, supporting the claim's point about grouping. It does not establish that most cyberbullying research does so, or compare that research with work on the wider LGBTQIA+ community or TGD individuals specifically. (LLM, conf=0.82)

    Experimental — accuracy not yet measured
  • LGBT adolescents are disproportionally more likely to be victims of cyberbullying than their heterosexual and cisgender peers.

    “LGBT adolescents are disproportionally more likely to be victims of cyberbullying than their heterosexual and cisgender peers [3,22,23]”

    From A Phenomenological Investigation into Cyberbullying as Experienced by People Identifying as Transgender or Gender Diverse (2022) · cites this paper as [23]

    The Results (Table 4; page not provided) show higher cyberbullying victimization among transgender students than cisgender peers, and the Discussion states this finding. The paper does not report a cyberbullying comparison between lesbian, gay, or bisexual adolescents and heterosexual peers; sexual orientation was an adjustment variable in the reported analysis. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Lian, Q., Li, R., Liu, Z., Li, X., Su, Q., & Zheng, D.
Affiliations
  • Fudan University (CN)
  • Capital Institute of Pediatrics (CN)
  • Jiangsu Provincial Center for Disease Control and Prevention (CN)
  • Tianjin Centers for Disease Control and Prevention (CN)
  • Shenzhen Children's Hospital (CN)
Corresponding author
  • Su, Qiru
  • Zheng, Dongpeng
Venue
BMC Public Health
Published
2022-04-05
Volume
22
Issue
1
Pages
650-650
ISSN
1471-2458 (ISSN-L)
OpenAlex
W4223422549
Cited by
28
References
34
Citations by year
2026: 5 · 2025: 13 · 2024: 3 · 2023: 3 · 2022: 4
FWCI
5.43
Type
journal article
Language
en
Open access
Locations
  • BMC Public Health (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Sanming Project of Medicine in Shenzhen (SZSM201612036)
Topics
  • Social Sciences › Psychology › Social Psychology › Bullying, Victimization, and Aggression
  • Social Sciences › Psychology › Social Psychology › LGBTQ Health, Identity, and Policy
  • Social Sciences › Gender Studies › Gender, Sexuality, and Education
Keywords
gender nonconformity, transgender identity, bullying victimization, cyberbullying, traditional bullying, high school students, gender diversity
MeSH terms
Adolescent, Cross-Sectional Studies, Female, Gender Identity, Humans, Infant, Newborn, Male, Risk-Taking, Crime Victims, Bullying

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review