Smoking Cessation Interventions and Abstinence Outcomes for People Living in Rural, Regional, and Remote Areas of Three High-Income Countries: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Tobacco smoking rates in high-income countries are greater in rural, regional, and remote (RRR) areas compared to cities. Yet, there is limited knowledge about interventions targeted to RRR smokers. This review describes the effectiveness of smoking cessation interventions for RRR smokers in supporting smoking abstinence. AIMS AND METHODS: Seven academic databases were searched (inception-June 2022) for smoking cessation intervention studies to include if they reported on RRR residents of Australia, Canada, or the United States, and short- (<6 months) or long-term (≥6 months) smoking abstinence outcomes. Two researchers assessed study quality, and narratively summarized findings. RESULTS: Included studies (n = 26) were primarily randomized control (12) or pre-post (7) designs, from the United States (16) or Australia (8). Five systems change interventions were included. Interventions included cessation education or brief advice, and few included nicotine monotherapies, cessation counseling, motivational interviewing, or cognitive behavioral therapy. Interventions had limited short-term effects on RRR smoking abstinence, decreasing markedly beyond 6 months. Short-term abstinence was best supported by contingency, incentive, and online cessation interventions, and long-term abstinence by pharmacotherapy. CONCLUSIONS: Cessation interventions for RRR smokers should include pharmacotherapy and psychological cessation counseling to establish short-term abstinence, and identify effective means of maintaining abstinence beyond 6 months. Contingency designs are a suitable vehicle for psychological and pharmacotherapy support for RRR people who smoke, and intervention tailoring should be explicitly considered. IMPLICATIONS: Smoking disproportionately harms RRR residents, who can encounter access barriers to smoking cessation support. High-quality intervention evidence and outcome standardization are still required to support long-term RRR smoking abstinence.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".