The Gaming Disorder Identification Test (GADIT) – A screening tool for Gaming Disorder based on ICD-11
Bibliographic record
Abstract
Background: Gaming Disorder was included as an addictive disorder in the latest version of the International Classification of Diseases (ICD-11), published in 2022. The present study aimed to develop a screening tool for Gaming Disorder, the Gaming Disorder Identification Test (GADIT), based on the four ICD-11 diagnostic criteria: impaired control, increasing priority, continued gaming despite harm, and functional impairment. Method: We reviewed 297 questionnaire items from 48 existing gaming addiction scales and selected 68 items based on content validity. Two datasets were collected: 1) an online panel (N = 803) from Australia, United States, United Kingdom and Canada, split into a development set (N = 589) and a validation dataset (N = 214); and 2) a university sample (N = 408) from Australia. Item response theory and confirmatory factor analyses were conducted to select eight items to form the GADIT. Validity was established by regressing the GADIT against known correlates of Gaming Disorder. Results: Confirmatory factor analyses of the GADIT showed good model fit (RMSEA=<0.001-0.108; CFI = 0.98-1.00), and internal consistency was excellent (Cronbach's alphas = 0.77-0.92). GADIT scores were strongly associated with the Internet Gaming Disorder Test (IGDT-10), and significantly associated with gaming intensity, eye fatigue, hand pain, wrist pain, back or neck pain, and excessive in-game purchases, in both the validation and the university sample datasets. Conclusion: The GADIT has strong psychometric properties in two independent samples from four English-speaking countries collected through different channels, and shown validity against existing scales and variables that are associated with Gaming Disorder. A cut-off of 5 is tentatively recommended for screening for Gaming Disorder.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".