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Record W2574019097 · doi:10.1177/1087054716685840

The Functional Impact of Sleep Disorders in Children With ADHD

2017· article· en· W2574019097 on OpenAlexaff
Stephanie G. Craig, Margaret D. Weiss, Kristen L. Hudec, Christopher Gibbins

Bibliographic record

VenueJournal of Attention Disorders · 2017
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsUniversity of British ColumbiaSimon Fraser University
Fundersnot available
KeywordsPsychologyInsomniaSleep (system call)Clinical psychologyAffect (linguistics)Quality of life (healthcare)PsychiatrySomnolenceExcessive daytime sleepinessRating scaleSleep disorderDevelopmental psychologyMedicineAdverse effect

Abstract

fetched live from OpenAlex

Objective: Children with ADHD display higher rates of sleep problems, and both sleep disorders and ADHD have been shown to affect functioning in childhood. The current study examines the frequency and relationship between sleep problems and ADHD, and their impact on quality of life (QoL) and functional impairment. Method: Parents of 192 children with ADHD ( M = 10.23 years) completed measures regarding their child’s ADHD symptoms (Swanson, Nolan and Pelham [SNAP]), sleep disorders (Pediatric Sleep Questionnaire [PSQ]), QoL (Child Health Illness Profile [CHIP-PE]), and functioning (Weiss Functional Impairment Rating Scale–Parent Report [WFIRS-P]). Results: Common sleep complaints in participants were insomnia, excessive daytime sleepiness (EDS), and variability in sleep schedule. Regression analysis indicated that sleep problems and ADHD symptoms independently predicted lower levels of QoL (Δ R 2 = .12, p < .001) and social functioning (Δ R 2 = .12, p < .001). Conclusion: The results suggest that ADHD may coexist with somnolence and that both conditions have a significant impact on a child’s functioning and QoL.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.020
GPT teacher head0.317
Teacher spread0.297 · 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

Citations90
Published2017
Admission routes1
Has abstractyes

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Same venueJournal of Attention DisordersSame topicAttention Deficit Hyperactivity DisorderFrench-language works237,207