Red Box, Green Box: Psychometric evaluation of a self‐report behavioral frequency measurement approach for behavioral addictions research
Bibliographic record
Abstract
AIMS: The behavioral addictions field lacks clinically useful behavior frequency measures. This study evaluated the psychometric performance of the new 'Red Box, Green Box' method for measuring gaming behavior with a focus on its utility for gaming disorder (GD) screening. DESIGN, SETTING AND PARTICIPANTS: A prospective, cross-sectional survey study was conducted using an online crowdsourcing platform. Participants were 1149 male gamers aged 18-35 years from Australia, Canada, United States, United Kingdom and Asia, reporting ≥12 hours of weekly gaming. MEASUREMENTS: Gaming time was measured using a conventional weekly hours item, Red Box hours (gaming instead of fulfilling responsibilities) and Green Box hours (gaming during free or leisure time). GD was assessed by the Internet Gaming Disorder Test (IGDT-10), with International Classification of Diseases, 11th Revision (ICD-11) and Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR) scoring approaches. Psychological distress [Depression, Anxiety and Stress Scale - 21 Items (DASS-21)] and impulsivity [Barratt Impulsiveness Scale-short form (BIS-15)] were measured. FINDINGS: Gamers with GD reported higher Red Box hours [mean (M) = 21.1, standard deviation (SD) = 11.3] than those without (M = 8.7, SD = 8.4; P < 0.001), and greater Red Box proportion (41.9% vs. 26.8%; P < 0.001). Red Box hours demonstrated superior diagnostic accuracy for GD [area under the curve (AUC) = 0.86, sensitivity = 0.94, specificity = 0.63] and Internet gaming disorder (IGD) (AUC = 0.76, sensitivity = 0.88, specificity = 0.56), outperforming comparative measures. A Red Box response of ≥ 9.5 hours had a 94% likelihood of indicating ICD-11 GD. CONCLUSIONS: The 'Red Box, Green Box' method appears to effectively identify International Classification of Diseases, 11th Revision, gaming disorder risk among males. Red Box hours demonstrated greater classification validity than the conventional weekly hours approach. This method provides a simple tool for epidemiological research, routine screening (e.g. outpatient consultation) and clinical assessment and treatment planning. Further validation in clinical populations and longitudinal studies is needed.
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 | 0.028 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".