The 20-item Gambling Harms Scale (GHS-20): Benchmarked to health utility using propensity weighting and control for comorbidities
Bibliographic record
Abstract
Measuring gambling-related harm is crucial for public health initiatives, but existing screens like the Problem Gambling Severity Index (PGSI) have limitations. The 10-item Short Gambling Harms Screen (SGHS/GHS-10) addresses some gaps, but lacks comprehensive domain coverage and robust validation against health utility benchmarks. This study aimed to develop and validate an extended 20-item Gambling Harms Scale (GHS-20). Objectives included achieving better representation across harm domains, selecting items based on unique associations with health utility decrements, and benchmarking the scale's scores against health utility. Data were collected from 2,603 Australian adults who gambled in the past year. Participants completed 31 candidate harm items, the PGSI, the WHOQOL-BREF (measuring quality of life), and the SF-12 (for SF-6D health utility derivation). Item selection utilised latent trait modelling and lasso regression. The final GHS-20 scale was benchmarked against SF-6D health utility using propensity score weighting to balance demographic factors and generalised additive modelling (GAM) to control for comorbidities. Lasso regression identified items providing unique explanatory information, resulting in the 20-item GHS-20, which includes items from all six gambling harm domains. The GHS-20 demonstrated excellent reliability (alpha=.98, omega=.90), correlated positively with the PGSI (r=.78) and negatively with health utility (r=-.38). GAM analysis revealed a significant, near-linear negative relationship between GHS-20 scores and health utility, with statistically significant decrements observed even at low scores. The GHS-20 is psychometrically robust, and can comprehensively assess gambling harm and calculate population harm burden.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.003 | 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".