Tobacco cessation strategies in military personnel: A meta-analysis of randomized trials
Bibliographic record
Abstract
To evaluate the effectiveness of behavioural and pharmacological tobacco cessation interventions among active-duty military personnel and veterans. We followed PRISMA guidelines to assess the efficacy of behavioural and pharmacological interventions on tobacco cessation in military populations. Randomized controlled trials were identified through PubMed (from 1966), Web of Science (from 1900), Scopus (from 1960), and the Cochrane Library/CENTRAL (from 1991) from database inception to July 2024. Random-effect models were used to estimate odds ratios and 95 % confidence intervals. Five randomized controlled trials involving a total of 2619 participants were included. Seven-day point prevalence abstinence was significantly increased at short-term (≤ 3 months) (OR 2.03 [95 % CI: 1.49, 2.77], low certainty) and long-term (≥ 6 months) (OR 1.53 [95 % CI: 1.12, 2.09], low certainty) follow-ups among those receiving interventions compared with controls. Subgroup analyses were conducted by type of personnel, tobacco product, intervention type, and sex. These findings highlight the need for stronger, accessible, and military-tailored cessation programs, particularly for veterans who show lower quit rates. Expanding high-quality randomized trials that test modern cessation approaches in diverse military populations is essential to inform future policy and clinical practice. • First systematic review and meta-analysis on tobacco cessation in military. • Tobacco cessation programs improve both short- and long-term quit rates. • Behavioural counseling and nicotine replacement therapy are most effective. • Veterans show lower quit success than active-duty military personnel. • Future research should assess modern cessation approaches in diverse groups.
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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 | 0.034 | 0.073 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.058 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".