Regular, intermittent, and spontaneous: Patterns of preschool Children's nap behavior and their correlates
Bibliographic record
Abstract
BACKGROUND: Daytime sleep during the preschool years (i.e., 1-5-years-old) is characterized by high inter-child variability in several components of nap behavior, including nap duration, nap timing, and the proportion of sleep during daytime. We used an empirical approach to examine variations amongst children in these aspects of napping and investigated correlates of these components of nap behavior. METHODS: A large, nationally representative sample (N = 702) of Canadian parents completed an online survey, including a one-month retrospective report of their 1.5-5 year old's daytime and nighttime sleep behavior and other questionnaires. To understand patterns of children's nap behaviors we applied Latent Profile Analysis (LPA) to typical nap duration, typical timing of naps, frequency of naps, proportion of sleep during the daytime, and the proportion of naps which were spontaneous (i.e., child just fell asleep). Then, multinominal logistic regression was used to examine correlates of profile membership. RESULTS: Four profiles of children emerged: (1) regular nappers; (2) intermittent nappers; (3) spontaneous nappers; and (4) non-nappers. After controlling for demographic variables (e.g., child age, sex, ethnicity) and known correlates of napping behaviors (e.g., birthweight, nighttime sleep duration), profile membership was related to parents' beliefs about napping, parents' own nap behaviors, family functioning, and child nighttime sleep problems in a multinominal logistic regression. CONCLUSIONS: An empirical approach aided in understanding the inter-child variability in napping amongst preschool-age children. Parental beliefs about napping and the home environment were shown to be critical factors influencing this variability.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".