The Measurement of Adult Problem and Pathological Gambling
Bibliographic record
Abstract
This paper presents a critical overview of measures used to assess adult problem gambling in clinical settings and general population research. Particular consideration is given to the challenges in transferring clinically derived measures into population research settings. Numerous screens developed for use in large population surveys as well as in non-specialist clinical settings are described in detail. Overall, the South Oaks Gambling Screen (SOGS) and its derivatives continue to be the most widely used measures in most contexts and parts of the world although the DSM-IV (Diagnostic and Statistical Manual of Psychiatric Disorders—IV) measures and the CPGI (Canadian Problem Gambling Index) are increasingly being used. While these measures are likely to continue in use, there are clear and growing indications that changes are needed to the official diagnosis of pathological gambling rather than to the measures that have been developed to assess gambling problems in population research and clinical settings. However, there is also room for improvement in these measures.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".