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Record W4297462932

Parental control on handphone access and usage among Malaysian children.

2022· article· en· W4297462932 on OpenAlexaboutno aff
M S Tan, H S Arvinder-Singh, W Y Lim, H S S Amar-Singh

Bibliographic record

VenuePubMed · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineQuarter (Canadian coin)LimitingParental controlPediatricsFamily medicineDemographyPsychologyDevelopmental psychology
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION: Parental control for a child's handphone access is important to ensure online safety. This study was to determine parental control on handphone access and the usage amongst Malaysian children. MATERIALS AND METHODS: A cross-sectional survey was conducted electronically between April 2017 and March 2018 among parents with children above 2 years of age, who owned a handphone. The 10-item questionnaire included questions about rules applied to the use of handphones, education on cybersafety, the characteristics and activities of their youngest children who had full-time access to a handphone, and parental perceptions of their children's usage of handphones. A total of 215 parents were included. RESULTS: From this, 92% controlled their children's handphones use by setting rules. The commonest rules were limiting the time of handphone usage (77%) and being aware of whom the child was communicating with (77%). The majority (94%) educated their children on cybersafety, and the commonest discussed topic was not to communicate with strangers (93%). The children's average age of first handphone ownership was 10.6 (SD: 3.6) years, and the use of the handphone averaged 17.4 (SD: 18.5) hours a week. Despite the rules and education provided, only a quarter of parents were confident of their children's capability to manage their own safety when using handphones (27%). CONCLUSION: In summary, Malaysian parents did control their children's handphone usage.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.081
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.238
Teacher spread0.222 · 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 teacher head, 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

Citations1
Published2022
Admission routes1
Has abstractyes

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