Estimating the prevalence of adult problem gambling in Italy with SOGS and PGSI
Bibliographic record
Abstract
Two assessment measures, the South Oaks Gambling Screen (SOGS), and the Problem Gambling Severity Index (PGSI), were administered to 1,979 participants (53% males, mean age 44.81 years). Results from exploratory and confirmatory factor analyses showed the presence of one single dimension underlying the SOGS and PGSI items. The 2 scales showed high levels of reliability. SOGS and PGSI results were highly correlated and showed positive and significant correlations with measures of gambling behaviour. Probable pathological gamblers identified by SOGS represented 2.05% (95% confidence interval 'CI' '1.17, 2.93') of the adult Italian population, and problem gamblers identified by PGSI represented 2.17% (95% CI '1.26, 3.07') of the population. A more conservative estimate of the prevalence of problem gambling in Italy, corresponding to 1.01% (95% CI '0.39, 1.63') of the adult population, was identified by considering only those participants for whom SOGS and PGSI were in perfect agreement concerning risk categories.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".