Promoting Evidence-Based Tobacco Cessation Treatment in Community Mental Health Clinics: Protocol for a Prepost Intervention Study
Bibliographic record
Abstract
BACKGROUND: Tobacco smoking is highly prevalent among persons with serious mental illness (SMI) and is the largest contributor to premature mortality in this population. Evidence-based smoking cessation therapy with medications and behavioral counseling is effective for persons with SMI, but few receive this treatment. Mental health providers have extensive experience working with clients with SMI and frequent treatment contacts, making them well positioned to deliver smoking cessation treatment. However, few mental health providers feel adequately trained to deliver this treatment, and many providers believe that smokers with SMI are not interested in quitting or have concerns about the safety of smoking cessation pharmacotherapy, despite substantial evidence to the contrary. OBJECTIVE: We present the protocol for the pilot "IMPACT" (Implementing Action for Tobacco Smoking Cessation Treatment) study, which aims to pilot test a multicomponent implementation intervention to increase the delivery of evidence-based tobacco smoking cessation treatment in community mental health clinics. METHODS: We are using a prepost observational design to examine the effects of an implementation intervention designed to improve mental health providers' delivery of the following four evidence-based practices related to smoking cessation treatment: (1) assessment of smoking status, (2) assessment of willingness to quit, (3) behavioral counseling, and (4) pharmacotherapy prescribing. To overcome key barriers related to providers' knowledge and self-efficacy of smoking cessation treatment, the study will leverage implementation strategies including (1) real-time and web-based training for mental health providers about evidence-based smoking cessation treatment and motivational interviewing, including an avatar practice module; (2) a tobacco smoking treatment protocol; (3) expert consultation; (4) coaching; and (5) organizational strategy meetings. We will use surveys and in-depth interviews to assess the implementation intervention's effects on providers' knowledge and self-efficacy, the mechanisms of change targeted by the intervention, as well as providers' perceptions of the acceptability, appropriateness, and feasibility of both the evidence-based practices and implementation strategies. We will use data on care delivery to assess providers' implementation of evidence-based smoking cessation practices. RESULTS: The IMPACT study is being conducted at 5 clinic sites. More than 50 providers have been enrolled, exceeding our recruitment target. The study is ongoing. CONCLUSIONS: In order for persons with SMI to realize the benefits of smoking cessation treatment, it is important for clinicians to implement evidence-based practices successfully. This pilot study will result in a set of training modules, implementation tools, and resources for clinicians working in community mental health clinics to address tobacco smoking with their clients. Trial Registration: ClinicalTrials.gov NCT04796961; https://clinicaltrials.gov/ct2/show/NCT04796961. TRIAL REGISTRATION: ClinicalTrials.gov NCT04796961; https://clinicaltrials.gov/ct2/show/NCT04796961. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/44787.
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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 | 0.052 | 0.039 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.086 | 0.017 |
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".