Use of a Rapid Qualitative Method to Inform the Development of a Text Messaging Intervention for People With Serious Mental Illness Who Smoke: Formative Research Study
Bibliographic record
Abstract
BACKGROUND: People with serious mental illness are disproportionately affected by smoking and face barriers to accessing smoking cessation treatments in mental health treatment settings. Text-based interventions are cost-effective and represent a widely accessible approach to providing smoking cessation support. OBJECTIVE: We aimed to identify key factors for adapting text-based cessation interventions for people with serious mental illness who smoke. METHODS: We recruited 24 adults from mental health programs who had a serious mental illness and currently smoked cigarettes or had quit smoking within the past 5 years. We then conducted virtual qualitative interviews between November 2020 and August 2021. Data were analyzed using the rapid thematic analytic approach. RESULTS: We identified the following 3 major themes: (1) interplay between smoking and having a serious mental illness, (2) social contextual factors of smoking in adults with serious mental illness, and (3) smoking and quitting behaviors similar to the general population. Participants reported barriers and facilitators to quitting across the 3 themes. Within the "interplay between smoking and having a serious mental illness" theme, barriers included smoking to manage stress and mental health symptoms, and facilitators to quitting included the awareness of the harm of smoking on mental health and patient-provider discussions on smoking and mental health. In the "social contextual factors of smoking in adults with serious mental illness" theme, barriers included high social acceptability of smoking among peers. Positive support and the combined social stigma of smoking and having a mental health condition outside of peer groups motivated individuals to quit. Some participants indicated that low exposure to other smokers during the COVID-19 pandemic helped them to engage in cessation efforts. In the "smoking and quitting behaviors similar to the general population" theme, barriers included smoking after eating, having coffee, drinking alcohol, and experiencing negative social support, and facilitators included health concerns, improvement in the general quality of life, and use of evidence-based tobacco treatments when available. CONCLUSIONS: People with serious mental illness often smoke to cope with intense emotional states, manage mental health symptoms, or maintain social bonds. Text message content emphasizing equally effective and less harmful ways for stress reduction and mental health symptom management may improve quit rates in individuals with serious mental illness.
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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 | 0.100 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".