Cyberbullying probability, not frequency, predicts mental health: a gendered investigation of individual, familial, and school-level predictors
Bibliographic record
Abstract
This study distinguishes between the probability and frequency of cyberbullying to examine its malleable predictors, mental health impacts, and gender differences among primary school children. We analysed data from 1031 students (49.75% male) and their parents across 19 primary schools in Hong Kong, employing a two-part model that distinguishes between the probability and frequency of cyberbullying experiences. The findings reveal that the probability of experiencing cyberbullying, rather than its frequency, was a significant predictor of poorer mental health in children. Higher digital literacy (DL), lower academic stress, and less frequent online activity were linked to reduced cyberbullying involvement for both boys and girls. Better family functioning was associated with lower rates of perpetration and victimisation among girls only. These findings offer a nuanced perspective on how individual, familial, and digital factors distinctly shape cyberbullying experiences and their mental health outcomes across genders in primary school students.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".