Gambling screens and problem gambling estimates: a parallel psychometric assessment of the South Oaks Gambling Screen and the Canadian Problem Gambling Index
Bibliographic record
Abstract
In 2005 the Northern Territory of Australia conducted its first population-based gambling and problem-gambling prevalence survey, administering both the South Oaks Gambling Screen (SOGS) and the Canadian Problem Gambling Index (CPGI) to the same sample of respondents. Using a sub-sample of regular gamblers (n=361), the respective problem gambling screens were subject to psychometric testing that included dimensionality, internal consistency, external validity, classification validity and screen order effects. Analyses were conducted for all regular gamblers stratified by gender. The CPGI produced a significantly lower prevalence estimate than the SOGS as well as lower rates of false-positives as measured against external criteria. Consistent with other studies, dimensionality analysis revealed a multi-dimensional factor structure for the SOGS and a single dimension for the CPGI. The CPGI displayed stronger correlations with external criteria and stronger internal consistency than the SOGS. A gender effect was observed, with both screens performing better for females. In addition, screen order significantly affected problem gambling prevalence estimates, although only for males and all persons. As a group, the psychometric analyses revealed that the results produced by the respective gambling screens are heavily context dependent, both in terms of methods of application and the characteristics of target populations. The key message of the paper is that post-hoc psychometric testing of gambling screens is essential in understanding the limitations of problem gambling prevalence estimates and to qualify and guide their interpretation when applied in general population surveys
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".