A retrospective analysis of the comparative effectiveness of smoking cessation medication among individuals with mental illness in community‐based mental health and addictions treatment settings
Bibliographic record
Abstract
WHAT IS KNOWN ON THE SUBJECT?: Persons with different mental illnesses smoke for reasons based on their particular diagnosis. As compared to those without, persons with mental illnesses are less able to quit smoking when using smoking cessation medications. WHAT THIS PAPER ADDS TO EXISTING KNOWLEDGE?: This paper shows that there may be differences in the ability to quit smoking between persons with different mental illness diagnoses. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: Clinicians should be aware that persons with anxiety disorders may find it more difficult to quit smoking as compared to those with other mental illnesses. Clinicians should be aware that of all medications, varenicline seems to help those with mood disorders to quit the best. Clinicians should be aware that persons with psychotic disorders likely need longer treatment durations for smoking cessation as compared to persons with other mental illnesses. ABSTRACT: Introduction Individuals with mental illnesses (MI) have diagnosis-specific reasons for smoking and achieve low smoking cessation when using cessation medications. Aim To assess differences in smoking cessation outcomes by MI diagnosis and cessation medications in outpatient mental health and addictions treatment settings in Vancouver, Canada. Method This is a retrospective analysis of tobacco treatment outcomes from 539 participants. The programme consists of cessation pharmacotherapy with 8 to 12 weeks of behavioural counselling and 12 weeks of support group. Smoking cessation was verified by expired carbon monoxide levels. Generalized estimating equations models assessed differences in cessation by type of medication in both total and stratified samples. Results There were no significant differences in cessation by pharmacotherapy in the total sample. Individuals with a mood disorder were two times more likely to achieve cessation as compared to those with an anxiety disorder. Among individuals with mood disorders, receiving varenicline alone resulted in three times the likelihood of cessation as compared to receiving single NRT. Discussion The differences in outcomes by MI diagnosis suggest the need for more diagnosis-specific approaches to optimize cessation. Implications for Practice Compared with other diagnoses, persons with anxiety disorders may have a greater challenge quitting and those with a psychotic disorder may require longer treatment durations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".