Psychometric evaluation of the NORC diagnostic screen for gambling problems (NODS) for the assessment of DSM-5 gambling disorder
Bibliographic record
Abstract
The National Opinion Research Center (NORC) Diagnostic Screen for Gambling Problems (NODS) is one of the most used outcome measures in gambling intervention trials. However, a screen based on DSM-5 gambling disorder criteria has yet to be developed or validated since the DSM-5 release in 2013. This omission is possibly because the criteria for gambling disorder only underwent minor changes from DSM-IV to DSM-5: the diagnostic threshold was reduced from 5 to 4 criteria, and the illegal activity criterion was removed. Validation of a measure that captures these changes is still warranted. The current study examined the psychometric properties of an online self-report past-year adaptation of the NODS based on DSM-5 diagnostic criteria for gambling disorder (i.e., NODS-GD). A diverse sample of participants (N = 959) was crowdsourced via Amazon's TurkPrime. Internal consistency and one-week test-retest reliability were good. High correlations (r = 0.74-0.77) with other measures of gambling problem severity were observed in addition to moderate correlations (r = 0.21-0.36) with related but distinct constructs (e.g., gambling expenditures, time spent gambling, other addictive behaviors). All nine of the DSM-5 criteria loaded positively on one principal component, which accounted for 40% of the variance. Classification accuracy (i.e., sensitivity, specificity, predictive power) was generally very good with respect to the PGSI and ICD-10 diagnostic criteria. Future studies are encouraged to establish a gold standard self-report measure of gambling problems and develop agreed-upon recommendations for the use and interpretation of crowdsourced addiction data.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".