Health effects of perceived racial and religious bullying among urban adolescents in China: A cross-sectional national study
Bibliographic record
Abstract
Research concerning ethnocultural bullying and adolescent health in China remains extremely limited. This study among Chinese urban adolescents examines associations between ethnocultural bullying and eight health-related outcomes: suicidal ideation, suicide planning, depressive symptomology, anxiety symptomatology, fighting, injury intentionally inflicted by another, smoking and moderate/heavy alcohol consumption. Data were obtained from the World Health Organisation's 2003 Chinese Global School-based Health Survey, a cross-sectional national survey of urban adolescents in four Chinese cities. The analytic sample size was n = 8182, which represented a sampling frame of 769,835 adolescents. Statistical analysis was conducted using generalised linear mixed effects models and sampling weights. Prevalence of ethnocultural bullying was significantly higher in Urumqi, Xinjiang province (2.08%) compared with Beijing municipality (0.72%) or Wuhan, Hubei province (0.67%). Compared to participants who were not bullied, religious bullying victimisation was significantly associated with suicidal ideation, injury intentionally inflicted by another and depressive symptomology. Racial bullying victimisation was significantly associated with suicidal ideation, injury intentionally inflicted by another and among females but not males, depressive symptomology. Health effects of ethnocultural bullying appear to be distinct from that of bullying in general. Additional research on ethnocultural adolescent health issues in China is warranted.
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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 | 0.001 |
| 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 | 0.000 | 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".