Use of mobile devices and psychosocial difficulties in children under 12 years of age: A systematic review and meta-analysis
Bibliographic record
Abstract
Abstract The growing use of mobile devices among children under the age of 12 underscores the need to investigate potential associated risks and to encourage appropriate usage habits. This meta-analysis aimed to examine the relationship between mobile device use and behavioral problems in children aged 3 to 12 years, as measured by the Strengths and Difficulties Questionnaire (SDQ). The literature search was conducted in three databases (PubMed, PsycINFO, and WOS), in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The study protocol was previously registered in the International Prospective Register of Systematic Reviews (PROSPERO). Eligibility criteria were established using the Population, Exposure, Comparator, and Outcomes (PECO) framework. Risk of bias in the included studies was assessed using the Newcastle–Ottawa Scale (NOS) for the quality appraisal of non-randomized studies. A total of seven studies were included in the meta-analysis. The results indicated an incidence of 8.52% for emotional problems (95% CI [4.10, 17.71]); 8.00% for behavioral problems (95% CI [1.87, 34.21]); 10.23% for symptoms of hyperactivity/inattention (95% CI [2.73, 38.40]); 11.64% for peer relationship problems (95% CI [5.20, 26.08]; k = 4); and 0.20% for reduced prosocial behavior (95% CI [0.12, 0.32]) among children with higher mobile device usage. Children’s greater use of mobile devices is associated with a higher prevalence of behavioral and emotional problems.
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 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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.045 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".