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Record W4386163616 · doi:10.3389/frsle.2023.1224610

Sleep, internalizing symptoms, and health-related quality of life in children with neurodevelopmental disorders: a cross-sectional analysis of cohort data from three research programs in Canada

2023· article· en· W4386163616 on OpenAlexafffundabout
Patrick G. McPhee, Stelios Georgiades, Andrea Andrade, Penny Corkum, Anthony L. Vaccarino, Heena Cheema, Rachel Chepesiuk, Alana Iaboni, Jan Willem Gorter

Bibliographic record

VenueFrontiers in Sleep · 2023
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsHolland Bloorview Kids Rehabilitation HospitalOntario Brain InstituteIndoc ResearchIzaak Walton Killam Health CentreDalhousie UniversityWestern UniversityMcMaster University
FundersGovernment of OntarioOntario Brain Institute
KeywordsDepression (economics)AnxietyCross-sectional studySleep disorderQuality of life (healthcare)MedicineSleep (system call)CohortPsychiatryPediatricsClinical psychologyPsychologyCognitionInternal medicine

Abstract

fetched live from OpenAlex

Objective The objectives of this study were to determine rates of sleep disturbances in children with neurodevelopmental disorders (NDDs) within and across disorders and compared to typically developing (TD) children and to describe differences above and below the clinical cut-off for sleep disturbances. In addition, we explored the associations between demographic variables, severity of disorder, sleep disturbances, internalizing symptoms, and health-related quality of life (HRQOL) in children with NDDs. Method We conducted cross-sectional data analyses of an existing database with community-dwelling children with NDDs ( n = 1438) and TD children ( n = 140) aged 4–12 years. Parent-reported measures on sleep disturbances using the Children's Sleep Habits Questionnaire (CSHQ), internalizing symptoms using the Revised Children's Anxiety and Depression Scale, and HRQOL using the KINDL-R were assessed. Hierarchical linear regression examined the associations between demographic variables, severity of disorder, sleep disturbances, internalizing symptoms, and HRQOL in children with NDDs. Results Children with NDDs (8.5 ± 2.1 years, 69.9% M) had significantly greater total sleep disturbance index (TSDI) than TD children [(8.6 ± 2.3 years, 60.0% M) (mean difference = 6.88 [95% CI 5.37, 8.40]; p < 0.001) ( n = 838 NDDs (58.3%); n = 120 TD (86.7%)]. Children with severe NDDs reported significantly greater TSDI above the clinical cut-off (i.e., ≥41; CSHQ) than those with less severe NDDs ( p < 0.001). Internalizing symptoms (β = −0.082 [95% CI −0.144, −0.019]; p = 0.011) and TSDI (β = −0.226 [95% CI −0.380, −0.073]; p = 0.004) were significantly associated with HRQOL in children with NDDs. Conclusion Surveillance and management of sleep and internalizing symptoms are needed to improve HRQOL in children with NDDs. Commonalities in sleep disturbances for children with NDDs support transdiagnostic interventions to treat sleep.

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)0.002
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 score0.047
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.088
GPT teacher head0.364
Teacher spread0.276 · 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

Citations3
Published2023
Admission routes3
Has abstractyes

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