The classification accuracy of four problem gambling assessment instruments in population research
Why this work is in the frame
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Bibliographic record
Abstract
Improved methodology was used to re-examine the weak correspondence between problem and pathological gamblers identified in population surveys and subsequent classification of these individuals in clinical interviews. The SOGS-R, the CPGI, the NODS and the Problem and Pathological Gambling Measure (PPGM), as well as questions about gambling participation and expenditures, were administered to a total of 7272 adults. Two clinicians then assessed each person's status, based on comprehensive written profiles derived from these questionnaire responses. Instrument classification was then compared to clinical classification. All four instruments correctly classified most non-problem gamblers (i.e. had good to excellent sensitivity, specificity and negative predictive power). However, the PPGM was the only instrument with good classification of problem gamblers (i.e. excellent sensitivity and positive predictive power). The CPGI and SOGS-R had weak positive predictive power and the NODS had only adequate sensitivity and positive predictive power. Improvement in the classification accuracy of the CPGI occurred when a 5+ cut-off was used and when a 4+ cut-off was used with the SOGS. In general, the classification accuracy of the NODS, SOGS and CPGI is better than prior research suggested but overall accuracy is still modest. With adjusted cut-offs, all three instruments are reasonably congruent with clinical ratings.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it