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Record W4205353734 · doi:10.2196/36568

Effect of Screen Time on Behavior of Preschoolers in Islamabad: Descriptive Cross-sectional Study

2022· article· en· W4205353734 on OpenAlexvenueno aff
Marriam Suleman, Ume Sughra

Bibliographic record

VenueIproceedings · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsnot available
Fundersnot available
KeywordsScreen timeTest (biology)PsychologyCronbach's alphaCross-sectional studyDevelopmental psychologyDescriptive statisticsEarly childhoodDemographyMedicineStatisticsPsychometrics

Abstract

fetched live from OpenAlex

Background The early years of childhood form the basis of intelligence, personality, social behavior, and the capacity to learn and nurture oneself as an adult. It is increasingly recognized that early and longer exposure to screens has adverse effects on the development of children. This research was significant in finding out the effects of screen time on the behavior of preschoolers, which could provide scientific grounds to the control of digital screen time. Objective We aimed to determine the effect of electronic exposure on the behavior, emotional development, and sleep quality of preschoolers and determine the average number of hours preschoolers spend with electronic devices in Islamabad. Methods A cross-sectional survey was conducted in 4 private preschools of Islamabad. A sample of 200 children aged 3 to 5 years was selected through multistage random sampling. The sociodemographic characteristics and screen time of the children were acquired by using parental questionnaires. Children were grouped based on a daily screen time of ≤60 minutes or >60 minutes. An analysis was conducted based on the results of the Child Behavior Checklist for children aged 1.5 to 5 years. The Cronbach α coefficient was found to be .925. It was analyzed by using SPSS version 22 (IBM Corporation). A chi-square test, an independent sample t test, and multilinear regression were applied to determine the associations and significance levels between the variables. Results The study results indicate that increased screen time was found to be statistically significant with regard to a child’s age, their education level, and the employment status of mothers. It was observed that preschoolers with a screen time of >60 minutes (mean 11.94, SD 3.91; P=.01) tend to more commonly experience withdrawn syndrome than those with a screen time of ≤60 minutes (mean 10.72, SD 3.01). Similarly, sleep problems were also more commonly observed in preschoolers with a screen time of >60 minutes (mean 10.97, SD 3.20; P=.01) when compared to those with a screen time ≤60 minutes (mean 9.90, SD 2.59). It was also observed that increased screen time had an association with autism spectrum problems among preschoolers with a screen time of >60 minutes (mean 17.66, SD 5.89; P=.047) when compared to those among preschoolers with a screen time of ≤60 minutes (mean 16.17, SD 4.58). The strongest predictor of the outcome variable was found to be mothers’ education level (ß=21.53). Conclusions The findings reveal that excessive screen time is a deleterious factor associated with the behavioral problems of preschoolers. Parents must also think about their child’s screen time. This requires parents’ active engagement and constant attention, so that the development and growth of their children are not affected adversely.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.316
Teacher spread0.296 · 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".

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Citations0
Published2022
Admission routes1
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