0933 Preschoolers Sleep Explains Parent Cognitions and Parenting Behaviors
Bibliographic record
Abstract
Abstract Introduction Children sleep is influenced by biological and socio-environmental factors. The contribution of various bedtime practices on child sleep is now well established. Emerging literature now seeks to understand the influence of more general parenting practices on child sleep. Thus, the current study examined how perceived children needs and parenting behaviors are associated with children sleep. Methods In a first study, 88 mothers (children 2-71 months) recruited during community activities completed the Child’s Sleep Habit Questionnaire, as well as a questionnaire regarding child needs (CN) and parents response to those needs (RN). Multiple linear regression analyses examined child sleep associations first to CN and then to RN, controlled by the child developmental score. Following this first study, 12 vulnerable mothers (children 39-68 months) participated in a child sleep intervention specifically developed for vulnerable families. The same measures were administered and Wilcoxon t-tests were calculated to compared pre and post intervention scores. Results Daytime sleepiness (β=.26, p=.008), sleep anxiety (β=.29, p=.007) and children cognitive development (β=-.32, p=.008) explained 31,6% of CN variance. Daytime sleepiness (β=.26, p=.03), and bedtime routine (β=-.68, p=.00), explained 20,8% of RN variance. Following the child sleep intervention, no change in CN were obtained, but a significant improvement in RN was found (Mpre=5.28 ± 1.60, Mpost=4.33 ± .65, p=.045). Conclusion Results suggest that parents perceived more needs in their child when they present higher daytime sleepiness, higher sleep anxiety and cognitive developmental difficulties. On the other hand, parents have more difficulty responding to their child needs when they see sleepiness in their child or struggle with bedtime routine. A child sleep intervention does not seem to change the perception of child needs but have a positive impact on the parent response to those needs. Support
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.002 | 0.001 |
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 teacher head, 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".