Reframing video gaming and internet use addiction: empirical cross‐national comparison of heavy use over time and addiction scales among young users
Bibliographic record
Abstract
BACKGROUND AND AIMS: Evidence-based and reliable measures of addictive disorders are needed in general population-based assessments. One study suggested that heavy use over time (UOT) should be used instead of self-reported addiction scales (AS). This study compared UOT and AS regarding video gaming and internet use empirically, using associations with comorbid factors. DESIGN: Cross-sectional data from the 2011 French Survey on Health and Consumption on Call-up and Preparation for Defence-Day (ESCAPAD), cross-sectional data from the 2012 Swiss ado@internet.ch study and two waves of longitudinal data (2010-13) of the Swiss Longitudinal Cohort Study on Substance Use Risk Factors (C-SURF). SETTING: Three representative samples from the general population of French and Swiss adolescents and young Swiss men, aged approximately 17, 14 and 20 years, respectively. PARTICIPANTS: ESCAPAD: n =22 945 (47.4% men); ado@internet.ch: n =3049 (50% men); C-SURF: n =4813 (baseline + follow-up, 100% men). MEASUREMENTS: We assessed video gaming/internet UOT ESCAPAD and ado@internet.ch: number of hours spent online per week, C-SURF: latent score of time spent gaming/using internet] and AS (ESCAPAD: Problematic Internet Use Questionnaire, ado@internet.ch: Internet Addiction Test, C-SURF: Gaming AS). Comorbidities were assessed with health outcomes (ESCAPAD: physical health evaluation with a single item, suicidal thoughts, and appointment with a psychiatrist; ado@internet.ch: WHO-5 and somatic health problems; C-SURF: Short Form 12 (SF-12 Health Survey) and Major Depression Inventory (MDI). FINDINGS: UOT and AS were correlated moderately (ESCAPAD: r = 0.40, ado@internet.ch: r = 0.53 and C-SURF: r = 0.51). Associations of AS with comorbidity factors were higher than those of UOT in cross-sectional (AS: .005 ≤ |b| ≤ 2.500, UOT: 0.001 ≤ |b| ≤ 1.000) and longitudinal analyses (AS: 0.093 ≤ |b| ≤ 1.079, UOT: 0.020 ≤ |b| ≤ 0.329). The results were similar across gender in ESCAPAD and ado@internet.ch (men: AS: 0.006 ≤ |b| ≤ 0.211, UOT: 0.001 ≤ |b| ≤ 0.061; women: AS: 0.004 ≤ |b| ≤ 0.155, UOT: 0.001 ≤ |b| ≤ 0.094). CONCLUSIONS: The measurement of heavy use over time captures part of addictive video gaming/internet use without overlapping to a large extent with the results of measuring by self-reported addiction scales (AS). Measuring addictive video gaming/internet use via self-reported addiction scales relates more strongly to comorbidity factors than heavy use over time.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".