1074 A Preliminary Analysis of the Association Between Sleep Quality and Behavior Following a Child Sleep Intervention
Bibliographic record
Abstract
Abstract Introduction It is well-established that sleep is essential for children’s physical, cognitive, and socioemotional development. Insufficient or poor-quality sleep has consistently been linked to behavioral difficulties in children. Interventions targeting parental sleep beliefs and practices may play a key role in improving both child sleep and related behavioral outcomes. This study aimed to examine whether improvements in children’s sleep quality following a short sleep intervention predict changes in behavioral outcomes. Methods A total of 74 parent-child dyads with children aged 3 to 5 participated in a two-hour group intervention on children’s sleep (2–4 families). While parents received the intervention, children engaged in sleep-related educational activities in another room. A short follow-up group session occurred two weeks later, attended only by the parents. Measures for this study were collected before the intervention (T1) and about two months later (T2). Parents completed various questionnaires, including the Children’s Sleep Habits Questionnaire (CSHQ) and the Strengths and Difficulties Questionnaire (SDQ). A hierarchical linear regression was conducted to determine whether changes in sleep quality (CSHQ scores) predicted children’s behavioral outcomes (SDQ scores) at T2. T1 behavioral difficulties and sleep quality were entered in the first block, while T2 sleep quality was added in the second block. The dependent variable was T2 behavioral difficulties. Results The first model was significant (F(2, 71) = 47.799, p <.001) and accounted for 57.4% of the variance. T1 behavioral difficulties predicted T2 behavioral difficulties (B =.682, p <.001), but T1 sleep quality was not a significant predictor (B =.133, p =.29). Adding T2 sleep quality explained an additional 3.1% of the variance (B =.355, p =.02). This change in R2 was significant (F(3, 70) = 35.644, p <.001), with the final model explaining 60.5% of the variance in T2 behavioral difficulties. Conclusion This study highlights the significant role of improving sleep quality in reducing behavioral difficulties in preschool-aged children. A short intervention targeting parental sleep beliefs and practices can effectively promote healthier sleep patterns and positive behavioral outcomes in young children. Support (if any) This project was funded by the SSHRC.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".