Digital Tools to Protect Young Children from Internet Addiction: Co-Designing with Children and Parents
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".