The Effectiveness of a Nursing Inpatient Smoking Cessation Program in Individuals With Cardiovascular Disease
Bibliographic record
Abstract
BACKGROUND: Smoking is an important risk factor for cardiovascular disease (CVD), and quitting is highly beneficial. Yet, less than 30% of CVD patients stop smoking. Relapse-prevention strategies seem most effective when initiated during the exacerbation of the disease. OBJECTIVE: A nurse-delivered inpatient smoking cessation program based on the Transtheoretical Model with telephone follow-up tailored to levels of readiness to quit smoking was evaluated on smoking abstinence and progress to ulterior stages of change. METHOD: Participants (N = 168) were randomly assigned by cohorts to inpatient counseling with telephone follow-up, inpatient counseling, and usual care. The inpatient intervention consisted of a 1-hr counseling session, and the telephone follow-up included 6 calls during the first 2 months after discharge. The nursing intervention was tailored to the individual's stage of change. End points at 2 and 6 months included actual and continuous smoking cessation rates (biochemical markers) and increased motivation (progress to ulterior stages of change). RESULTS: Assuming that surviving patients lost to follow-up were smokers, the 6-month smoking abstinence rate was 41.5% in the inpatient counseling with telephone follow-up group, compared with 30.2% and 20% in the inpatient counseling and usual care groups, respectively (p = .05). Progress to ulterior stages of change was 43.3%, 32.1%, and 18.2%, respectively (p = .02). Stage of change at baseline and intervention predicted smoking status at 6 months. DISCUSSION: This tailored smoking cessation program with telephone follow-up significantly increased smoking cessation at 6 months, and progression to ulterior stages of change. The telephone follow-up was an important adjunct. It is, therefore, recommended to include such comprehensive smoking cessation programs within hospital settings for individuals with CVD.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".