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 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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".