The relationship between player losses and gambling‐related harm: evidence from nationally representative cross‐sectional surveys in four countries
Bibliographic record
Abstract
BACKGROUND AND AIMS: Flaws in previous studies mean that findings of J-shaped risk curves for gambling should be disregarded. The current study aims to estimate the shape of risk curves for gambling losses and risk of gambling-related harm (a) for total gambling losses and (b) disaggregated by gambling activity. DESIGN: Four cross-sectional surveys. SETTING: Nationally representative surveys of adults in Australia (1999), Canada (2000), Finland (2011) and Norway (2002). PARTICIPANTS: A total of 10 632 Australian adults, 3120 Canadian adults, 4484 people aged 15-74 years in Finland and 5235 people aged 15-74 years in Norway. MEASUREMENTS: Problem gambling risk was measured using the modified South Oaks Gambling Screen, the NORC DSM Screen for Gambling Problems and the Problem Gambling Severity Index. FINDINGS: Risk curves for total gambling losses were estimated to be r-shaped in Australia {β losses = 4.7 [95% confidence interval (CI) = 3.8, 6.5], β losses(2 =) -7.6 (95% CI = -17.5, -4.5)}, Canada [β losses = 2.0 (95% CI = 1.3, 3.9), β losses(2 =) -3.9 (95% CI = -15.4, -2.2)] and Finland [β losses = 3.6 (95% CI = 2.5, 7.5), β losses(2 =) -4.4 (95% CI = -34.9, -2.4)] and linear in Norway [β losses = 1.6 (95% CI = 0.6, 3.1), β losses(2 =) -2.6 (95% CI = -12.6, 1.4)]. Risk curves for different gambling activities showed either linear, r-shaped or non-significant relationships. CONCLUSIONS: Player loss-risk curves for total gambling losses and for different gambling activities are likely to be linear or r-shaped. For total losses and electronic gaming machines, there is no evidence of a threshold below which increasing losses does not increase the risk of harm.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".