Factors Affecting Smoking Cessation Efforts of People With Severe Mental Illness: A Qualitative Study
Bibliographic record
Abstract
OBJECTIVE: People with severe mental illness are much more likely to smoke than are members of the general population. Smoking cessation interventions that combine counseling and medication have been shown to be moderately effective, but quit rates remain low and little is known about the experiences of people with severe mental illness in smoking cessation interventions. To address this gap in knowledge, we conducted a qualitative study to investigate factors that help or hinder the smoking cessation efforts of people with severe mental illness. METHODS: We recruited 16 people with severe mental illness who had participated in a clinical trial of two different smoking cessation interventions, one involving nicotine replacement therapy only and the other nicotine replacement therapy combined with motivational interviewing and a peer support group. We conducted open-ended, semi-structured interviews with participants, who ranged in age from 20 to 56 years old, were equally distributed by gender (eight men and eight women), and were predominantly Caucasian (n = 13, 81%). Primary mental illness diagnoses included schizophrenia/schizoaffective disorder (n = 6, 38%), depression (n = 5, 31%), bipolar disorder (n = 4, 25%), and anxiety disorder (n = 1, 6%). At entry into the clinical trial, participants smoked an average of 22.6 cigarettes per day (SD = 13.0). RESULTS: RESULTS indicated that people with mental illness have a diverse range of experiences in the same smoking cessation intervention. Smoking cessation experiences were influenced by factors related to the intervention itself (such as presence of smoking cessation aids, group supports, and emphasis on individual choice and needs), as well as individual factors (such as mental health, physical health, and substance use), and social-environmental factors (such as difficult life events and social relationships). CONCLUSIONS: An improved understanding of the smoking cessation experiences of people with severe mental illness can inform the delivery of future smoking cessation interventions for this population. The results of this study suggest the importance of smoking cessation interventions that offer a variety of treatment options, incorporating choice and flexibility, so as to be responsive to the evolving needs and preferences of individual clients.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".