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Record W2145039187 · doi:10.1002/icd.527

A model for predicting behavioural sleep problems in a random sample of Australian pre‐schoolers

2007· article· en· W2145039187 on OpenAlexaff
Wendy A. Hall, Stephen R. Zubrick, Sven Silburn, Deborah E. Parsons, Jennifer J. Kurinczuk

Bibliographic record

VenueInfant and Child Development · 2007
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologyChecklistSleep (system call)Developmental psychologyPopulationChild Behavior ChecklistDepression (economics)DistressClinical psychologyDemography

Abstract

fetched live from OpenAlex

Abstract Behavioural sleep problems (childhood insomnias) can cause distress for both parents and children. This paper reports a model describing predictors of high sleep problem scores in a representative population‐based random sample survey of non‐Aboriginal singleton children born in 1995 and 1996 (1085 girls and 1129 boys) in Western Australia. Longitudinal repeated data were collected up to age 4 years by caregiver report. Children's sleep rhythmicity levels in their first year, as well as conflicted and lax parenting in their second year, predicted higher scores on the sleep problem scale from the Child Behaviour Checklist/2–3 in the children's third year. Higher scores on the sleep problem scale in the children's third year predicted higher scores on the aggressive behaviour subscale of the Child Behaviour Checklist/4–16. The results support a model in which sleep problems mediated the relationship between parental conflict and aggressive behaviour, even when controlling for maternal depression, which has been associated with children's aggressive behaviour. Copyright © 2007 John Wiley & Sons, Ltd.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.025
GPT teacher head0.279
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations48
Published2007
Admission routes1
Has abstractyes

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