Prevalence and correlates of bullying perpetration and victimization among school-aged youth with intellectual disabilities: A systematic review [accepted manuscript]
Bibliographic record
Abstract
Recent literature reviews show that bullying perpetration and victimization are major public health concerns for typically developing (TD) youth. Nevertheless, the magnitude of this phenomenon among youth with intellectual disabilities (ID) remains unclear. Therefore, the purpose of this review is to provide a synthesis of the empirical studies examining the prevalence and correlates of bullying perpetration and victimization among youth with ID. A systematic literature search was performed and 11 studies met the inclusion criteria. The findings from these studies showed weighted mean prevalence rates of general bullying perpetration, bullying victimization and both of 15.1%, 36.3%, and 25.2%, respectively. Weighted mean prevalence rates of bullying perpetration and victimization differed according to the characteristics of the studies (e.g., assessment context, school setting, information source, type of measures, time frame). Additionally, high weighted mean prevalence rates of physical (33.3%), verbal (50.2%), relational (37.4%), and cyber (38.3%) victimization were found among youth with ID. When youth with ID were compared to youth with other disabilities or TD peers, no clear differences were found. Finally, the present review shows that correlates of bullying perpetration and victimization in this population remain understudied.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".