Effectiveness of the Tobacco Tactics Program in the Department of Veterans Affairs
Bibliographic record
Abstract
PURPOSE: The purpose was to determine the effectiveness of the Tobacco Tactics program in three Veterans Affairs hospitals. METHODS: In this effectiveness trial, inpatient nurses were educated to provide the Tobacco Tactics intervention in Ann Arbor and Detroit, while Indianapolis was the control site (N = 1,070). Smokers were surveyed and given cotinine tests. The components of the intervention included nurse counseling, brochure, DVD, manual, pharmaceuticals, 1-800-QUIT-NOW card, and post-discharge telephone calls. RESULTS: 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 Indianapolis. Pre- versus post-intervention quit rates were 4 % compared to 13 % in Detroit, were similar (6 %) pre- and post-intervention in Ann Arbor, and dropped from 26 % to 12 % in Indianapolis. CONCLUSION: The Tobacco Tactics program, which meets the Joint Commission standards that apply to all inpatient smokers, has the potential to significantly decrease smoking among Veterans.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".