Occurrence and Predictors of Challenging Behavior in Youth with Intellectual Disability with or without Autism
Bibliographic record
Abstract
Introduction: Challenging behaviors are common in individuals with developmental disabilities and pose significant challenges to their well-being and that of their families and communities.Method: We explored the occurrence and predictors of self-injurious behavior and aggressive/destructive behavior in 372 Canadian youths (aged 4–20 years) with a moderate to severe intellectual disability (ID), with or without autism. Data were collected through the Great Outcomes for Kids Impacted by Severe Developmental Disabilities (GO4KIDDS) basic survey.Results: Parent-report survey data indicated that 56% of the sample had displayed aggressive/destructive behavior and 39% self-injurious behavior over the past 2 months. Both behaviors were significantly more common in youths with ID plus autism compared to ID alone. There was an interaction between diagnosis and adaptive behavior in relation to aggressive/destructive behavior. For those with ID alone, lower adaptive functioning was predictive of aggressive/destructive behavior, whereas for those with ID plus autism, higher adaptive functioning was predictive.Conclusion: Overall, a large proportion of children in the sample were reported to engage in at least one challenging behavior. These findings highlight the importance of differential diagnosis, and an assessment of adaptive function to ascertain the prognosis of challenging behavior. Study limitations and avenues for future research are discussed.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".