Which Are the Optimal Screening Tools for Internet Use Disorder in Children and Adolescents? A Systematic Review of Psychometric Evidence
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.
Bibliographic record
Abstract
Abstract Purpose of Review The early detection of problematic Internet use (PIU) is essential to prevent the development of Internet use disorders (IUD). Although a variety of screening tools have already been developed and validated for this purpose, yet a consensus about optimal IUD assessment is still lacking. In this systematic review, we (i) describe the identified instruments for children and adolescents, (ii) critically examine their psychometric properties, and (iii) derive recommendations for particularly well-validated instruments. Recent Findings We conducted a systematic literature search in five databases on January 15, 2024. Of the initial 11,408 references identified, 511 studies were subjected to a full-text analysis resulting in a final inclusion of 70 studies. These studies validated a total of 31 instruments for PIU and IUD, including the Diagnostic Interview for Internet Addiction (DIA), a semi-structured interview. In terms of validation frequency, the Internet Addition Test (IAT) had the largest evidence base, followed by the Compulsive Internet Use Scale (CIUS). Only two of the measures examined were based on the current DSM-5 criteria for Internet Gaming Disorder. Summary Although no screening instrument was found to be clearly superior, the strongest recommendation can be made for CIUS, and Generalized Problematic Internet Use Scale (GPIUS2). Overall, the quality of the included studies can only be rated as moderate. The IUD research field would benefit from clear cut-off scores and a clinical validation of (screening) instruments.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it