0931 Parenting Stress Can Be Predicted by Parent and Child Sleep, and Can Be Reduced by an Intervention Targeting Child Sleep in Vulnerable Families
Bibliographic record
Abstract
Abstract Introduction Studies suggest that family intervention programs should consider different sources of parenting stress (PS). Knowing that child sleep has an impact on PS, this study aimed to examine if a child sleep intervention can affect PS in vulnerable families. Methods In a first study, parent and child sleep habits along with PS were assessed using Parenting Stress Index-Short Form, Pittsburgh Sleep Quality Index and homemade questions assessing child sleep. Measures were administered to 138 community mothers (children 3-79 months). Multiple linear regression analyses examined mothers and child sleep associations to PS, controlled by family income. Following results of this first study, 11 vulnerable mothers (children 39-68 months) participated in a child sleep intervention specifically developed for vulnerable families. Measures administered were the same as for study 1, but in this study, the Child Sleep Habits Questionnaire was used to assess the child sleep. Wilcoxon’s t-test compared pre and post-intervention measures. Results In the first study, sleep onset latency (SOL; β= -.25; p= .003), waketime (β= -.28; p= .003) and sleep aids (β= -.19; p= .048) explained 23.6% of PS variance. When child sleep variables were added to the model, only bedtime resistance was significant (β= -.23; p= .023) and increased the explained variance to 30.6%. Following the child sleep intervention, no significant changes in parent sleep were observed. However, results show improvement of child bedtime resistance (Mpre=10.73; Mpost=8.36; p= .027) and PS (Mpre=3.50; Mpost=3.82; p= .05). Conclusion Our results suggest that higher PS is explained by greater struggles in parents sleep (higher SOL, later waketime and higher use of sleep aids) as well as bedtime resistance behaviors in children. Furthermore, PS can be reduced by an intervention improving child bedtime resistance, even if parent sleep is not improved. Support
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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 | 0.000 | 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) | 0.003 | 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".