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Record W2802302768 · doi:10.1177/1074840718773381

Sleep Outcomes for Parents of Children With Neurodevelopmental Disabilities: A Systematic Review

2018· review· en· W2802302768 on OpenAlexafffund
Samantha Micsinszki, Marilyn Ballantyne, Kristin Cleverley, Pamela Green, Robyn Stremler

Bibliographic record

VenueJournal of Family Nursing · 2018
Typereview
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsHolland Bloorview Kids Rehabilitation HospitalUniversity of Toronto
FundersOntario Ministry of Research and InnovationCanadian Institutes of Health Research
KeywordsSleep (system call)PsychologyMedicineDevelopmental psychologyPsychiatryClinical psychology

Abstract

fetched live from OpenAlex

Parents of children with Neurodevelopmental Disabilities (NDDs) are at risk of sleep loss. No comprehensive systematic reviews examining parental sleep outcomes in caregivers of children with NDDs exist. A systematic search was conducted between June and August 2016 examining sleep quantity, quality, sleepiness, and fatigue outcomes of caregivers of children with NDDs. Of 7,534 citations retrieved, 33 met eligibility criteria. Most studies ( n = 27) were cross-sectional, included a range of NDDs and were of "poor" ( n = 14) or "fair" ( n = 17) quality. Few good quality studies compared objectively measured sleep in parents of children with NDDs with parents with typically developing children. Parents of children with NDDs consistently reported significantly poorer subjective sleep quality. There is a paucity of good quality comparative studies, using well-validated measures, examining parental sleep outcomes. Future research should aim to fill this gap, providing greater insight to parents' experiences, and identifying targets for intervention design and evaluation.

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)0.003
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0060.005
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.123
GPT teacher head0.443
Teacher spread0.320 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations30
Published2018
Admission routes2
Has abstractyes

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Same venueJournal of Family NursingSame topicFamily and Disability Support ResearchFrench-language works237,207