Revenue Associated With Gambling-Related Harm as a Putative Indicator for Social Responsibility: Results From the Swiss Health Survey
Bibliographic record
Abstract
Gambling behaviours represent a significant social and economic cost and an important public health problem. A putative index for monitoring gambling-related harm is a concentration of spending indicator that reports the proportion of gambling revenue derived from problem gambling. Using this indicator, we aimed to provide a first estimate of the proportion of gambling revenue associated with gambling-related harm in Switzerland according to the Swiss Health Survey. Data were obtained from the Swiss Health Survey 2017. The National Opinion Research Centre Diagnostic and Statistical Manual of Mental Disorders – Loss of Control, Lying and Preoccupation (NODS-CLiP) screening tool was used as part of the questionnaire, and the study findings were evaluated to determine the prevalence of gambling-related harm. Self-reported spending on terrestrial and online gambling (including gaming tables, electronic gaming machines, lotteries, sports betting) during the past 12 months was then used to calculate the portion of gambling revenue derived from players experiencing harm. A total of 12,191 respondents were included. Gambling-related harm was reported by 3.10% of our sample, according to NODS-CLiP criteria. The findings showed that although 52% of people experiencing harm spend less than 100 francs per month on gambling, 31.3% of total spending is attributable to gambling-related harm. In addition to pre-existing national prevalence studies, data on spending should be made readily available by gambling operators and regulators, in keeping with their regulatory obligations. The revenue structure, according to gambling type, should also be provided, including data from third-party gambling operators. In an interdisciplinary effort to improve public health and consumer protection, organized national structural prevention measures should be developed and evaluated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".