Effectiveness of the Tobacco Tactics program in the Department of Veterans Affairs
Bibliographic record
Abstract
Background: Smoking cessation interventions during hospitalization have been shown to be efficacious, yet are rarely incorporated into practice. The purpose of this study was to determine the effectiveness of the Tobacco Tactics program in three Veterans Affairs (VA) hospitals. Materials and methods: In this quasi-experimental pre- post- comparison effectiveness trial, inpatient nurses were educated to provide the Tobacco Tactics intervention in the Ann Arbor, MI and Detroit, MI VA hospitals, while the Indianapolis, IN VA hospital was the control site (N=1,070). The Tobacco Tactics nurse toolkit included: 1) one contact hour for training; 2) a PowerPoint presentation on behavioral and pharmaceutical interventions; 3) a pocket card “Helping Smokers Quit: A Guide for Clinicians”; 4) pharmaceutical and behavioral protocols; and 5) a computerized template for nurse documentation. The patient toolkit included: 1) a brochure; 2) a videotape “Smoking: Getting Ready to Quit;” 3) a Tobacco Tactics manual; 4) pharmaceuticals; 5) a 1-800-QUIT-NOW help line card; and 6) post-discharge telephone calls. Smoking patients were surveyed in the hospital and again six-months post-discharge. Urinary cotinine tests were used to verify six-month smoking status. Results: The average age was 55.3 years, most were male (94%) and not married (76%). After adjustment for the propensity of being assigned to treatment condition, there were significant improvements in 6-month quit rates in the pre- to post-intervention time periods in Ann Arbor (p=0.004) and Detroit (p<0.001) compared to the Indianapolis control site. The intervention was particularly effective in Detroit where pre-intervention quit rates were 4% compared to 13% post-intervention. Conclusions: This study showed that training staff nurses to integrate smoking cessation services into their routine care may increase quit rates. The Tobacco Tactics program, which meets the newly released (2011) Joint Commission standards that apply to all inpatient smokers, has the potential to significantly decrease smoking among patients admitted to VA hospitals.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".