Associations between probable anxiety and mood disorder and measures of alcohol and cannabis use in young, middle-aged and older adults.
Bibliographic record
Abstract
This study examined the associations of cannabis use, alcohol use and alcohol problems with probable anxiety and mood disorders (AMD) in young, middle-aged and older adults. Method: Data are based on the CAMH Monitor, an ongoing cross-sectional telephone survey of Ontario adults aged 18 years and older. For the purposes of the current study, a merged dataset from the years 2001 through 2009 inclusive was separated into three individual datasets: 18-34 year olds (n=4,211), 35-54 year olds (n=7,874), and 55 years of age and older (n=6,778). The survey included the 12-item version of the General Health Questionnaire, which provides a measure of probable AMD for the general population. Logistic regression analyses examined the odds of probable AMD in three age groups associated with alcohol measures (number of drinks per day and alcohol problems (AUDIT 8+)) and cannabis use, while controlling for self-reported physical health, religious service attendance, and demographic factors. Due to listwise deletion, the logistic regression models were based on reduced samples. Results: Lifetime cannabis use and past year cannabis use predicted probable AMD in young and middle-aged adults, but only lifetime cannabis use predicted probable AMD among older adults. Alcohol problems predicted probable AMD among middle aged and older adults, but not among younger adults. No consistent link between recent alcohol consumption and probable AMD was observed. Conclusion: These analyses suggest that the impact of alcohol and cannabis use and problems on probable AMD may differ across age groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".