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Record W2945767479 · doi:10.3390/bs9050056

Caregiver Reports of Screen Time Use of Children with Autism Spectrum Disorder: A Qualitative Study

2019· article· en· W2945767479 on OpenAlexaff
Anja Stiller, Jan Weber, Finja Strube, Thomas Mößle

Bibliographic record

VenueBehavioral Sciences · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsSNC-Lavalin (Canada)
FundersBundesministerium für Bildung und Forschung
KeywordsAutism spectrum disorderPsychologyAutismMedia useScreen timeQualitative researchDevelopmental psychologyClinical psychologyMedicineSocial psychologyPhysical activity

Abstract

fetched live from OpenAlex

Screen based media have progressively become an integral part in the daily lives of children and youths with and without autism spectrum disorder (ASD). However, research that exclusively pursues the functionality of screen media use of children with ASD is extremely rare. Through a triangulated approach, the present study aims to fill this gap. We conducted 13 interviews with parents of children with ASD and supplemented this interview-study with an online survey including parents of children with ASD (n = 327). Children with ASD mostly used screen media (especially television) for their wellbeing, which is associated with chances and risks. Based on the parental interviews it is suggested that the media usage of children with ASD should be supervised. The results are discussed in terms of their practical implementation.

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)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0040.003
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
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 head0.042
GPT teacher head0.350
Teacher spread0.308 · 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 designQualitative
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

Citations28
Published2019
Admission routes1
Has abstractyes

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