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Record W3194182583 · doi:10.3389/fpsyt.2021.702774

Characterizing Changes in Screen Time During the COVID-19 Pandemic School Closures in Canada and Its Perceived Impact on Children With Autism Spectrum Disorder

2021· article· en· W3194182583 on OpenAlexaffabout
Robyn E. Cardy, Annie Dupuis, Evdokia Anagnostou, Justine Ziolkowski, Elaine Biddiss, Suneeta Monga, Jessica Brian, Melanie Penner, Azadeh Kushki

Bibliographic record

VenueFrontiers in Psychiatry · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsHospital for Sick ChildrenPublic Health OntarioUniversity of TorontoHolland Bloorview Kids Rehabilitation Hospital
Fundersnot available
KeywordsScreen timeAutism spectrum disorderMental healthMedicineCoronavirus disease 2019 (COVID-19)AutismPandemicDemographyPsychologyPsychiatryPhysical activityPhysical therapy

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has led to an increase in screen time for children and families. Traditionally, screen time has been associated with negative physical and mental health outcomes, and children with autism spectrum disorder (ASD) are at increased risk of these outcomes. The primary objectives of this study were to (1) characterize the change in screen time during COVID-19 school closures for children with ASD, and (2) examine the parent perceived impact of screen time on mental health and quality of life of children and their families. Canadian parents and caregivers of children 19 years of age and younger were eligible to participate in an anonymous, online survey study. This survey was available in English, consisted of 28 questions, took ~10-min to complete, and was available for 6 weeks (May 22 through July 6, 2020). The total sample consisted of 414 responses (ASD: n = 127, mean age = 11.7 ± 4.06 years; community sample: n = 287, mean age = 9.4 ± 4.26 years). Seventy-one respondents were missing responses to our primary question and removed from the analyses (final sample n = 344). Compared to the community sample, the ASD group had a significantly higher screen time use before and during the COVID-19 pandemic school closures [weekdays: difference = 1.14 (SE = 0.18), t = 6.56, p < 0.0001; weekends: difference = 1.41 (SE = 0.20), t = 6.93, p < 0.0001]. Mean total screen time during the pandemic was 6.9 h (95% CI 6.49, 7.21) on weekdays and 6.3 h (95% CI 5.91, 6.63) on weekends for the ASD group, and 5.6 h (95% CI 5.28, 5.92) on weekdays and 5.0 h (95% CI 4.70, 5.34) on weekends for the community sample. There was a significant increase in screen time during the COVID-19 pandemic as compared to before the pandemic period in the ASD group [weekdays: mean difference = 3.8 h (95% CI 3.35–4.25), p < 0.0001; weekends: mean difference = 1.5 h (95% CI 1.17–1.92), p < 0.0001]. Gender was a significant predictor of parent perceived mental health and quality of life, with male gender associated with a higher likelihood of negative impact [quality of life (child/family) OR = 1.8 (95% CI 1.1–2.9), corrected p = 0.040; mental health OR = 1.9 (95% CI 1.1–3.1), corrected p = 0.0028]. Parents' most frequently endorsed emotions toward screen time were guilt, frustration, and worry. Results of this survey study revealed that children with ASD were less likely to benefit from screen time to cope with social isolation, and screen time resulted in significantly more lost time on social interactions than the community sample, which may exacerbate difficulties in social domains. Given the unprecedented circumstances of the COVID-19 pandemic and the novel context of technology use, the findings of this study highlight the need for revision of screen time recommendations to reflect the current needs of children and families.

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 score0.029
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.007
GPT teacher head0.243
Teacher spread0.235 · 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

Citations40
Published2021
Admission routes2
Has abstractyes

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