Examining the predictive validity of low‐risk gambling limits with longitudinal data
Bibliographic record
Abstract
AIMS: To assess the impact of gambling above the low-risk gambling limits developed by Currie et al. (2006) on future harm. To identify demographic, behavioural, clinical and environmental factors that predict the shift from low- to high-risk gambling habits over time. DESIGN: Longitudinal cohort study of gambling habits in community-dwelling adults. SETTING: Alberta, Canada. PARTICIPANTS: A total of 809 adult gamblers who completed the time 1 and time 2 assessments separated by a 14-month interval. MEASUREMENTS: Low-risk gambling limits were defined as gambling no more than three times per month, spending no more than CAN$1000 per year on gambling and spending less than 1% of gross income on gambling. Gambling habits, harm from gambling and gambler characteristics were assessed by the Canadian Problem Gambling Index. Ancillary measures of substance abuse, gambling environment, major depression, impulsivity and personality traits assessed the influence of other risk factors on the escalation of gambling intensity. FINDINGS: Gamblers classified as low risk at time 1 and shifted into high-risk gambling by time 2 were two to three times more likely to experience harm compared to gamblers who remained low risk at both assessments. Factors associated with the shift from low- to high-risk gambling behaviour from time 1 to time 2 included male gender, tobacco use, older age, having less education, having friends who gamble and playing electronic gaming machines. CONCLUSIONS: An increase in the intensity of gambling behaviour is associated with greater likelihood of future gambling related harm in adults.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".