Deriving low‐risk gambling limits from longitudinal data collected in two independent Canadian studies
Bibliographic record
Abstract
AIMS: To derive low-risk gambling limits using the method developed by Currie et al. (2006) applied to longitudinal data. DESIGN: Secondary analysis of data from the Quinte Longitudinal Study (n = 3054) and Leisure, Lifestyle and Lifecycle Project (n = 809), two independently conducted cohort studies of the natural progression of gambling in Canadian adults. SETTING: Community-dwelling adults in Southeastern Ontario and Alberta, Canada. PARTICIPANTS: A total of 3863 adults (50% male; median age = 44) who reported gambling in the past year. MEASUREMENTS: Gambling behaviours (typical monthly frequency, total expenditure and percentage of income spent on gambling) and harm (experiencing two or more consequences of gambling in the past 12 months) were assessed with the Canadian Problem Gambling Index. FINDINGS: The dose-response relationship was comparable in both studies for frequency of gambling (days per month), total expenditure and percentage of household income spent on gambling (area under the curve values ranged from 0.66 to 0.74). Based on the optimal sensitivity and specificity values, the low-risk gambling cut-offs were eight times per month, $75CAN total per month and 1.7% of income spent on gambling. Gamblers who exceeded any of these limits at time 1 were approximately four times more likely to report harm at time 2 [95% confidence interval (CI) = 2.9-6.6]. CONCLUSIONS: Longitudinal data in Canada suggest low-risk gambling thresholds of eight times per month, $75CAN total per month and 1.7% of income spent on gambling, all of which are higher than previously derived limits from cross-sectional data. Gamblers who exceed any of the three low-risk limits are four times more likely to experience future harm than those who do not.
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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 | 0.024 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".