Criterion-related and construct validity of the Problem Gambling Severity Index in a sample of South African gamblers
Bibliographic record
Abstract
The Problem Gambling Severity Index, the scored module of the Canadian Problem Gambling Index, is a population-based survey instrument that is becoming the preferred epidemiological tool for estimating the prevalence of disordered gambling. While some validation evidence for the Problem Gambling Severity Index is available, very little is known about its psychometric characteristics in developing countries or in countries the populations of which are not highly Westernised. The aim of this study was to investigate the validity of the Problem Gambling Severity Index with a specific focus on its criterion-related and construct (concurrent) validity in a community sample of gamblers in South Africa ( n = 127). To this end, the Problem Gambling Severity Index was administered alongside the Diagnostic Interview for Gambling Severity and measures known to associate with gambling severity (impulsivity, current debt, social problems, financial loss, race, sex). Results showed that the Problem Gambling Severity Index was predictive of Diagnostic Interview for Gambling Severity diagnosis from both a categorical and dimensional point of view and demonstrated high discrimination accuracy for subjects with problem gambling. Analysis of sensitivity and specificity at different cut-points suggests that a slightly lower Problem Gambling Severity Index score may be used as a screening cut-off for problem gambling among South African gamblers. The Problem Gambling Severity Index also showed significant correlations with the Barratt Impulsiveness Scale, a widely known measure of impulsivity, and with some of the predicted behavioural variables of interest (gambling activities, money lost to gambling, current debt, interpersonal conflict). This article therefore demonstrates initial criterion and concurrent validity for the Problem Gambling Severity Index for use in South African samples.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".