Beyond challenging behaviors, sleep maintenance problems in autistic youth at the time of hospitalization are associated with increased caregiver strain
Bibliographic record
Abstract
PURPOSE: Autistic children are at increased risk for psychiatric hospitalization for challenging behaviors. Sleep problems may increase this risk due to the added strain they place on caregivers and their ability to maintain safety at home. We evaluate if caregiver-reported sleep problems in autistic children at the time of the child's hospital admission predict caregiver reported stress and self-efficacy, above and beyond the severity of the child's challenging behaviors. METHODS: Participants included 598 autistic children (ages 4-20) admitted to specialized psychiatric inpatient units and a primary caregiver. Caregivers reported if their child had sleep initiation and/or sleep maintenance problems. Caregivers completed the Parenting Stress Index-Short Form and Difficult Behavior Self-Efficacy Scale. Hierarchical linear regression models examined associations between sleep problems and caregiver outcomes, controlling for child irritability, and both household- and child-level characteristics. RESULTS: Fifty-nine percent of children had a caregiver-reported sleep problem. Sleep maintenance problems were significantly associated with increased parental distress, more dysfunctional parent-child interactions, and greater perception of the child as difficult to manage, above and beyond challenging behavior severity and other covariates. Sleep initiation problems were unrelated to caregiver outcomes. Self-efficacy in single caregivers and those in lower-income households was more negatively impacted by sleep problems than other caregivers. CONCLUSIONS: Sleep maintenance problems may independently contribute to caregiver strain above and beyond challenging behaviors. Social and financial resources may buffer against the negative effect of sleep problems. Preventative interventions targeting sleep may provide needed support for vulnerable families.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".