Characteristics of people with mental disorders differ between currently smoking and ceased smoking
Bibliographic record
Abstract
Abstract Introduction Although the prevalence of smoking is higher among people with mental disorders compared to those without mental disorders, people with mental disorders are less successful for smoking cessation. This study examined the variation in characteristics of people with mental disorders across those who are current smokers and former smokers. Methodology This study used the Public Used Microdata File of the Canadian Community Health Survey 2012. (n = 25,113). People with any mental health disorder in the last 12 months were identified using the World Health Organization Composite International Diagnostic Interview instrument. Smoking status was classified based on self-report responses as: current, former, and never smoking. Multivariable logistic regression analysis was used to examine the association between the characteristics of people with mental disorders and smoking cessation (vs continuation). Results Overall, the prevalence of current smoking, former smoking, and nonsmoker were 37.5%, 33.6%, and 28.8% respectively. Immigrants compared to Canadian-born (OR = 0.6, 95% CI = 0.3, 0.8) and those who were single (either widowed or divorced or separated or single) compared to married or living with a partner (OR = 0.4, 95% CI = 0.1, 0.6) were less likely to quit smoking. Similarly, less educated and young people were also less likely to quit smoking. Conclusions Young people, living alone, less educated, and immigrants are less successful to quit smoking. Findings indicate the social disparity in smoking cessation among people with mental disorders. This may have been related to the barriers in accessing smoking cessation support among this group. Key messages Findings underscore the disparity in smoking cessation among people with mental disorder. Implementation of tailored, personalized smoking cessation support may be helpful to address the challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".