Evaluating three problem gambling screens: SOGS, VGS and CPGI
Bibliographic record
Abstract
This paper presents a comparative evaluation of three problem gambling screens: the Victorian Gambling Screen (VGS), the Canadian Problem Gambling Index (CPGI) and the South Oaks Gambling Screen (SOGS, version 5+). It is based on a population survey of 8,479 Victorian residents commissioned by the Victorian Gambling Research Panel (GRP). Using methods of concurrent validation, the study undertook 'content analysis' of the three screens to explore conceptual issues; analysis of 'item distribution' and 'difficulty'; 'factor analyses' to test the dimensionality of the problem gambling screens; analyses of 'internal consistency'; and assessment of 'construct validity' to examine correlative relationships between screen scores and correlates of problem gambling. While finding limitations with all three screens, overall the study found that the CPGI demonstrated the best measurement properties of all three gambling instruments. As well as essential questions about screen validity, the paper will discuss issues for future consideration in Australian prevalence studies of problem gambling.
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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 | 0.015 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".