Prevalence of School Bullying Among Youth with Autism Spectrum Disorders: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
The true extent of school bullying among youth with autism spectrum disorders (ASD) remains an underexplored area. The purpose of this meta-analysis is to: (a) assess the proportion of school-aged youth with ASD involved in school bullying as perpetrators, victims or both; (b) examine whether the observed prevalence estimates vary when different sources of heterogeneity related to the participants' characteristics and to the assessment methods are considered; and (c) compare the risk of school bullying between youth with ASD and their typically developing (TD) peers. A systematic literature search was performed and 17 studies met the inclusion criteria. The resulting pooled prevalence estimate for general school bullying perpetration, victimization and both was 10%, 44%, and 16%, respectively. Pooled prevalence was also estimated for physical, verbal, and relational school victimization and was 33%, 50%, and 31%, respectively. Moreover, subgroup analyses showed significant variations in the pooled prevalence by geographic location, school setting, information source, type of measures, assessment time frame, and bullying frequency criterion. Finally, school-aged youth with ASD were found to be at greater risk of school victimization in general, as well as verbal bullying, than their TD peers. Autism Res 2016, 9: 601-615. © 2015 International Society for Autism Research, Wiley Periodicals, Inc.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.022 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".