Smokers with Self-Reported Mental Health Conditions: A Case for Screening in the Context of Tobacco Cessation Services
Bibliographic record
Abstract
BACKGROUND: People with mental health conditions (MHC) smoke at high rates and many die prematurely from smoking-related illnesses. Smoking cessation programs, however, generally do not screen for MHC. This study examined the utility of MHC screening in a large tobacco quitline to determine whether self-reported MHC predicts service utilization and quitting behaviors. METHODS & FINDINGS: A brief set of question on MHC was embedded in the routine intake of a state quitline, and 125,261 smokers calling from June 2012 to September 2015 were asked the questions. Quit attempt rate and 6-month success rate were analyzed for a random subset of participants. Overall, 52.2% of smokers reported at least one MHC. Demographic patterns like gender or ethnic difference in self-reported MHC were similar to that in the general population. Depression disorder was reported most often (38.6%), followed by anxiety disorder (33.8%), bipolar disorder (17.0%), drug/alcohol abuse (11.9%), and schizophrenia (7.9%). Among those reporting any MHC, about two-thirds reported more than 1 MHC. Smokers with MHC received more counseling than smokers with no MHC. Quit attempt rates were high for all three groups (>70%). The probability of relapse was greater for those with more than one MHC than for those with one MHC (p<0.005), which in turn was greater than those with no MHC (p < .01). The six-month prolonged abstinence rates for the three conditions were, 21.8%, 28.6%, and 33.7%, respectively. The main limitation of this study is the use of a non-validated self-report question to assess MHC, even though it appears to be useful for predicting quitting behavior. CONCLUSIONS: Smokers with MHC actively seek treatment to quit. Smoking cessation services can use a brief set of questions to screen for MHC to help identify smokers in need of more intensive treatment to quit smoking.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.010 | 0.013 |
| 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".