Striving Towards National Lower-Risk Gambling Guidelines: An Empirical Investigation Among a Sample of Swedish Gamblers
Bibliographic record
Abstract
Several countries, including Canada and Australia, have developed public health-based lower-risk gambling limits to differentiate lower-risk from higher-risk gambling. This study aimed to identify a preliminary set of lower-risk gambling limits (gambling frequency, duration, expenditure, expenditure as a proportion of personal net income, and diversity), and investigate if gambling types are linked to additional harms, in a Swedish context. The study involved secondary analyses of two online survey studies using the Gambling Disorder Identification Test (GDIT). Receiver operating curve analyses were conducted in relation to both + 1 and + 2 gambling-related harms in a sample of 705 past-year gamblers. Potential lower-risk limits ranges identified were: gambling frequency of "2-3 times a week" to "4 or more times a week" (8-16 times monthly); gambling duration of 6 to 15 h per month; gambling expenditure of 2,000 SEK (approximately $USD190) per month; gambling expenditure as a proportion of personal net income of 5%; and gambling diversity of only one problematic gambling type. Gambling on slots and sports betting were associated with gambling-related harms. The lower-risk limits in the current study were higher than in previous studies, which may be explained by the large proportion of support- or treatment-seeking gamblers with high rates of problem gambling and problematic online gambling in the study sample. An international consensus-based framework on gambling consumption is warranted, with lower-risk limits validated in future empirical studies using larger datasets collected from the Swedish general population.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".