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Record W4412554180 · doi:10.1080/10447318.2025.2527846

Digital Tools to Protect Young Children from Internet Addiction: Co-Designing with Children and Parents

2025· article· en· W4412554180 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Human-Computer Interaction · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsImpact
Fundersnot available
KeywordsAddictionThe InternetPsychologyInternet privacyDevelopmental psychologyPsychiatryComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Internet addiction emerges as a substantial health and well-being issue in children. Research suggests that software-mediated interventions may help address the problem; however, there is a gap in investigating the designs and considerations from the perspectives of children and parents, who are the primary stakeholders. The present study aims to co-design digital tools to protect young children from internet addiction. We involved 24 participants (children and parents) in 3 serial workshops: (1) conceptualising potential efforts through focus group discussion, (2) designing digital tools through card-based ideation, and (3) evaluating ideas through mixed-method testing. Our study contributes to conceptualising seven themes of potential efforts and producing 18 digital tool features categorised into eight functions. We discovered the promising potential of digital tools as parent-child dyadic interventions to encourage real-world interests, promote positive online activities, schedule balanced activities, provide online rules as playful missions with constructive consequences, and assist parental mediation decision-making.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.996

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 studies0.0000.000
Scholarly communication0.0010.001
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 head0.017
GPT teacher head0.312
Teacher spread0.295 · 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