Sleep Problems in Children With Attention-Deficit/Hyperactivity Disorder
Bibliographic record
Abstract
OBJECTIVES: To determine the prevalence of sleep problems in children with attention-deficit/hyperactivity disorder (ADHD) and their associations with child quality of life (QOL), daily functioning, and school attendance; caregiver mental health and work attendance; and family functioning. DESIGN: Cross-sectional survey. SETTING: Pediatric hospital outpatient clinic, private pediatricians' offices, and ADHD support groups in Victoria, Australia. PARTICIPANTS: Schoolchildren with ADHD. Main Exposure Attention-deficit/hyperactivity disorder. OUTCOME MEASURES: Primary measure was caregivers' reports of their children's sleep problems (none, mild, or moderate or severe). Secondary outcomes were (1) child QOL (Pediatric Quality of Life Inventory), daily functioning (Daily Parent Rating of Evening and Morning Behavior scale), and school attendance, (2) caregiver mental health (Depression Anxiety Stress Scale) and work attendance, and (3) family functioning (Child Health Questionnaire subscales). Caregivers also reported on how their pediatrician treated their children's sleep problems. RESULTS: Two hundred thirty-nine of 330 (74%) eligible families completed the survey. Child sleep problems were common (mild, 28.5%; moderate or severe, 44.8%). Moderate or severe sleep problems were associated with poorer child psychosocial QOL, child daily functioning, caregiver mental health, and family functioning. After adjusting for confounders, all associations held except for family impacts. Compared with children without sleep problems, those with sleep problems were more likely to miss or be late for school, and their caregivers were more likely to be late to work. Forty-five percent of caregivers reported that their pediatricians had asked about their children's sleep and, of these, 60% reported receiving treatment advice. CONCLUSIONS: Sleep problems in children with ADHD are common and associated with poorer child, caregiver, and family outcomes. Future research needs to determine whether management of sleep problems can reduce adverse outcomes.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".