Variation in characteristics of people with mental disorders across smoking status in the Canadian general population
Bibliographic record
Abstract
INTRODUCTION: People with mental disorders are less successful in smoking cessation efforts. This study compared the characteristics of current smokers and former smokers with mental disorders. METHODS: This was a cross-sectional study that used the Public Use Microdata File of the Canadian Community Health Survey 2012. Survey respondents with any mental health disorder in the last 12 months (n=2700), identified using the World Health Organization Composite International Diagnostic Interview instrument, were included in the analysis. Smoking status was classified based on self-report responses as current, former and never smoker. Logistic regression models were used to analyze the data. RESULTS: The odds of quitting smoking were significantly lower among people who were single or never married (widowed/divorced/separated/single) compared to those who were married or had a common-law partner (adjusted odds ratio, AOR=0.6, 95% CI: 0.4-0.9). Similarly, significantly lower odds of quitting smoking were observed among people with less than post-secondary education compared to those with post-secondary education (AOR=0.4, 95% CI: 0.3- 0.6). Also, the odds of quitting were significantly lower among immigrants, young adults, and middle-aged adults. CONCLUSIONS: People who are young or middle-aged, single or never married, less educated, and immigrants, are less likely to quit smoking. This pattern underscores the socioeconomic disparities in quitting smoking among people with mental disorders. Future research should investigate why these groups continue to smoke more often than their counterparts. This will help design the smoking cessation support that address the challenges experienced by vulnerable populations and reduce the disparities.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".