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 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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".