Validation of the Gambling Perceived Stigma Scale (GPSS) and the Gambling Experienced Stigma Scale (GESS)
Bibliographic record
Abstract
Australian research shows that stigma is a major barrier to treatment seeking (Rockloff, 2004) and may impede the accurate measurement of problem gambling prevalence. To date, no validated tool is available to assess the stigma associated with gambling. This project investigated both internally experienced and externalised (perceived) stigma associated with gambling, as measured with two new survey instruments were developed for this purpose. We reviewed existing measures of stigma associated with other non-gambling behaviours (e.g., alcohol, drug abuse, smoking, eating disorders) to construct items that were conceptually related to gambling behaviour. The scales were then validated by using a large representative community sample (N = 1366). Internal reliability analysis, factor analysis, and multivariate analysis were used to analyse the results and to explore the measurement of perceived and self-stigma in a community sample, taking into account respondents' gambling experience and relevant socio-demographic information. Results supported a model of perceived stigma along two dimensions (Contempt and Ostracism) and a unidimensional model of experienced stigma. The scales were shown to have strong psychometric properties and to differentiate well between stigmas associated with recreational and problem gambling behaviours. A scale that measures stigma related to gambling behaviour will provide researchers, policymakers, industry bodies, and clinicians with a tool that contributes to a growing understanding of the gambling experiences of individuals and the impacts of gambling on communities.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".