Treatment of Tobacco Dependence in Mental Health and Addictive Disorders
Bibliographic record
Abstract
People with mental health and addictive (MHA) disorders smoke at high rates and require tobacco treatment as a part of their comprehensive psychiatric care. Psychiatric care providers often do not address tobacco use among people with mental illness, possibly owing to the belief that their patients will not be able to quit successfully or that even short-term abstinence will adversely influence psychiatric status. Progress in the development of treatments has been slow in part because smokers with current MHA disorders have been excluded from most smoking cessation trials. There are several smoking cessation treatment options, including psychological and pharmacological interventions, that should be offered to people with an MHA disorder who smoke. Building motivation and readiness to quit smoking is a major challenge, and therefore motivational interventions are essential. We review the treatment options for people with tobacco dependence and MHA disorders, offer recommendations on tobacco assessment and tailored treatment strategies, and provide suggestions for future research. Treatment efficacy could be enhanced through promoting smoking reduction as an initial treatment goal, extending duration of treatment, and delivering it within an integrated care model that also aims to reduce the availability of tobacco in MHA treatment settings and in the community.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".