Area Variations in the Prevalence of Substance Use and Gambling Behaviours and Problems in Quebec: A Multilevel Analysis
Bibliographic record
Abstract
OBJECTIVES: This study aimed to examine whether variations among regions in Quebec existed after we controlled for individual characteristics in the prevalence of 1) alcohol, cannabis, and gambling behaviours and 2) substance-related disorders and pathological gambling. METHODS: Using data derived from the Canadian Community Health Survey: Mental Health and Well-Being (CCHS 1.2), we nested 5332 respondents from the province of Quebec within 374 regions equivalent to census subdivisions (CSDs). Outcome variables included 1) drinking status (past 12 months), alcohol consumption (last week), and 12-month diagnosis of alcohol dependence; 2) cannabis use (past 12 months and lifetime) and diagnosis of illicit drug dependence; and 3) gambling status, severity of gambling problems, and number of reported gambling activities (past 12 months). Multilevel regression models with individuals (Level 1) nested in regions (CSDs, Level 2) assessed the variations among regions in the prevalence of various outcomes and disorders when individual characteristics were controlled for. RESULTS: Variance component models revealed that all alcohol-related variables, the prevalence of cannabis use (12 months), and problem gambling did not vary among areas. Gambling rates and the average number of reported gambling activities varied among areas, even when individual-level variables were accounted for in the models, whereas for lifetime cannabis use, variations among areas became nonsignificant. CONCLUSION: Intervention programs may need to address the environment as a relevant determinant of health-related behaviours and lifestyles.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".