School Bullying Victimization, Perpetration, Witness and Health‐Related Quality of Life (HRQoL): The Difference Between Boys and Girls
Bibliographic record
Abstract
ABSTRACT This paper investigated how different combinations of school bullying involvement (victimization, perpetration and witness) affected adolescents' HRQoL, with a focus on gender differences. A total of 3675 students from seven big cities across China were recruited. HRQoL was assessed by the KIDSCREEN‐10 scale, and the bullying involvements were divided into eight types based on the criteria of whether a student was being bullied, being a perpetrator or being a witness. The prevalence of the eight types of school bullying involvements, ‘uninvolved’, ‘victim only’, ‘perpetrator only’, ‘witness only’, ‘victim‐perpetrator’, ‘victim‐witness’, ‘perpetrator‐witness’, ‘victim‐perpetrator‐witness’, were 59.84%, 12.38%, 10.07%, 8.68%, 5.55%, 1.82%, 0.95% and 0.71%, respectively. Students who were ‘victim only’ ( β = −1.79, 95% CI = [−2.69,−0.88]), ‘victim‐perpetrator’ ( β = −3.87, 95% CI = [−5.71,−2.03]), ‘victim‐witness’ ( β = −2.01, 95% CI = [−2.86,−1.16]) and ‘victim‐perpetrator‐witness’ ( β = −3.41, 95% CI = [−4.52,−2.30]) had a significantly much lower degree of HRQoL in comparison with the ‘uninvolved’. The correlations between bullying involvement and HRQoL demonstrated different patterns between boys and girls. The victimization experience of school bullying harms adolescents' HRQoL most, while the perpetrator and witness experience can amplify these effects. Students with multiple roles, especially the victim‐perpetrator‐witness and the victim‐perpetrator, are more vulnerable than others regarding HRQoL.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".