The 10-item and 20-item gambling harms scale for affected others (GHS-10-AO, GHS-20-AO): benchmarked to health utility using propensity weighting and control for comorbidities
Bibliographic record
Abstract
Gambling-related harms significantly impact not only gamblers but also people socially connected to them (affected others or AOs), yet quantifying these impacts has remained challenging. This study developed and validated two scales for measuring harm to people due to someone else's gambling - the 10-item and 20-item Gambling Harms Scale for Affected Others (GHS-10-AO and GHS-20-AO) - benchmarked to health utility metrics. Using data from 2,018 Australian adults with close relationships to gamblers, we employed psychometric item selection, propensity weighting, and control for comorbidities to establish evidence for causal links between reported harms and health utility decrements measured by the SF-6D. Emotional, relational, and financial harms were the most prevalent items selected. Both scales demonstrated excellent reliability (α = 0.89 for GHS-10-AO; α = 0.94 for GHS-20-AO) and strong correlations with health utility measures (r = -0.47 to -0.48 with SF-6D). The relationship between harm scores and health utility showed significant non-linearity, with increasing convexity at higher harm levels. These scales provide the first validated instruments for quantifying health impacts to AOs using a common metric comparable to gambler-focused harm measures, enabling population-level assessment of current gambling harm in the adult population; inclusive of gamblers and connected others. The instruments fill a critical gap in gambling harm measurement and offer jurisdictions tools for monitoring progress toward harm minimisation that encompasses impacts on both gamblers and those around them.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".