Investigating the Convergence between Actigraphy, Maternal Sleep Diaries, and the Child Behavior Checklist as Measures of Sleep in Toddlers
Bibliographic record
Abstract
The current study examined associations among actigraphy, maternal sleep diaries, and the parent-completed child behavior checklist (CBCL) sleep items. These items are often used as a sleep measure despite their unclear validity with young children. Eighty middle class families (39 girls) drawn from a community sample participated. Children (M = 25.34 months, SD = 1.04) wore an actigraph monitor (Mini-Mitter(®) Actiwatch Actigraph, Respironics) for a 72-h period, and mothers completed a sleep diary during the same period. Eighty-nine percent of the mothers and 75% of the fathers also filled out the CBCL (1.5-5). Mother and father CBCL scores were highly correlated. Overall, good correspondence was found between the CBCL filled out by mothers and sleep efficiency and duration derived from maternal sleep diaries (r between -0.39 and -0.25, p ≤ 0.05). Good correspondence was also found between the CBCL filled out by fathers and sleep efficiency as derived from maternal sleep diaries (r between -0.39 and -0.24, p ≤ 0.05), but not with sleep duration (all results were non-significant). Very few correlations between actigraphy and the CLBL scores reached statistical significance. The Bland and Altman method revealed that sleep diaries and actigraphy showed poor agreement with one another when assessing sleep duration and sleep efficiency. However, diary- and actigraphy-derived sleep durations were significantly correlated. Consistent with findings among older groups of children, this study suggests that the CBCL sleep items, sleep diaries, and actigraphy tap into quite different aspects of sleep among toddlers. The choice of which measures to use should be based on the exact aspects of sleep that one aims to assess. Overall, despite its frequent use, the composite sleep score of the CBCL shows poor links to objective measures of sleep duration and sleep efficiency.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".