Influence of co‐occurring mental and substance use disorders on the prevalence of problem gambling in Canada
Bibliographic record
Abstract
CONTEXT/BACKGROUND: Research has shown that problem gambling (PG) is associated with substance use disorders (SUD) and also with other mental disorders (MD). Nevertheless, evidence about the relative contribution of each type of disorder for the risk of gambling in the population is very limited. OBJECTIVE: Study the association of SUD, alone and in combination with MD, with the prevalence and severity of PG. DESIGN: Cross-sectional national survey (Canadian Community Health Survey-Mental Health and Well-Being) data collected through a multi-stage stratified cluster design. SETTING: Population-based household survey. PARTICIPANTS: This analysis includes data on 36 885 participants (99.7% of the survey sample). MAIN OUTCOME MEASURES: The prevalence and severity of PG were measured using the Canadian Problem Gambling Index. Prevalence of MD (mood and anxiety disorders) and SUD were defined according to the World Mental Health Survey Initiative Composite International Diagnostic Interview, following definitions of the DSM-IV. RESULTS: Compared to the population, higher prevalence rates of PG are observed when the severity of SUD is higher, but are not impacted by the co-occurrence of MD. For individuals with low risk and moderate risk/problem gambling, the prevalence rate difference (prevalence rate in the subgroup minus prevalence rate in the population) observed among substance dependents was reduced when MD co-occurred (from a prevalence rate difference of 2.5; 99% confidence interval 1.6-3.8 to 1.6; 99% confidence interval 1.2-2.2 for low risk gamblers and from 3.7; 99% confidence interval 1.6-5.5 to 2.9; 99% confidence interval 2.0-4.3 for moderate risk/problem gamblers). Estimates were not statistically different. CONCLUSIONS: Prevalence of all levels of PG increased with SUD severity, but the pattern did not appear to be affected by MD co-occurrence. Results suggest particular attention be given to SUD in treatment-seeking clients with co-occurring disorders.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".