Development of the Gambling Disorder Identification Test (G-DIT): Protocol for a Delphi Method Study
Bibliographic record
Abstract
BACKGROUND: Research on the identification and treatment of problem gambling has been characterized by a wide range of outcome measures and instruments. However, a single instrument measuring gambling behavior, severity, and specific deleterious effects is lacking. OBJECTIVE: This protocol describes the development of the Gambling Disorder Identification Test (G-DIT), which is a 9- to 12-item multiple-choice scale with three domains: gambling consumption, symptom severity, and negative consequences. The scale is analogous to the widely used Alcohol Use Disorders Identification Test (AUDIT) and the Drug Use Disorders Identification Test (DUDIT). METHODS: The G-DIT is developed in four steps: (1) identification of items eligible for the G-DIT from a pool of existing gambling measures; (2) presentation of items proposed for evaluation by invited expert researchers through an online Delphi process and subsequent consensus meetings; (3) pilot testing of a draft of the 9- to 12-item version in a small group of participants with problem gambling behavior (n=12); and (4) evaluation of the psychometric properties of the final G-DIT measure in relation to the existing instruments and self-reported criteria of the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5), among individuals with problem gambling and nonproblematic recreational gambling behaviors (n=600). This protocol article summarizes step 1 and describes steps 2 and 3 in detail. RESULTS: As of October 2018, steps 1-3 are complete, and step 4 is underway. CONCLUSIONS: Implementation of this online Delphi study early in the psychometric development process will contribute to the face and construct validity of the G-DIT. We believe the G-DIT will be useful as a standard outcome measure in the field of problem gambling research and serve as a problem-identification tool in clinical settings. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/12006.
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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 | 0.075 | 0.078 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.090 | 0.028 |
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".