Presence of problematic and disordered gambling in older age and validation of the South Oaks Gambling Scale
Bibliographic record
Abstract
The use of instruments originally developed for measuring gambling activity in younger populations may not be appropriate in older age individuals. The aim of this study was to examine the presence of problematic and disordered gambling in seniors aged 50 or over, and study the reliability and validity properties of the SOGS (a screening measure to identify gambling related problems). Two independent samples were recruited: a clinical group of n = 47 patients seeking treatment at a Pathological Gambling Outpatient Unit, and a population-based group of n = 361 participants recruited from the same geographical area. Confirmatory factor analysis verified the bifactor structure for the SOGS with two correlated underlying dimensions [measuring the impact of gambling on the self primarily (Cronbach's alpha α = 0.87) or on both the self and others also (α = 0.82)], and a global dimension of gambling severity (also with excellent internal consistency, α = 0.90). The SOG obtained excellent accuracy/validity for identifying gambling severity based on the DSM-5 criteria (area under the ROC curve AUC = 0.97 for discriminating disordered gambling and AUC = 0.91 for discriminating problem gambling), and good convergent validity with external measures of gambling (Pearson's correlation R = 0.91 with the total number of DSM-5 criteria for gambling disorder, and R = 0.55 with the debts accumulated due to gambling) and psychopathology (R = 0.50, 0.43 and 0.44 with the SCL-90R depression, anxiety and GSI scales). The optimal cutoff point for identifying gambling disorder was 4 (sensitivity Se = 92.3% and specificity Sp = 98.6%) and 2 for identifying problem gambling (Se = 78.8% and Sp = 96.7%). This study provides empirical support for the reliability and validity of the SOGS for assessing problem gambling in elders, and identifies two specific factors that could help both research and clinical decision-making, based on the severity and consequences of the gambling activity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| 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".