Behaviour change techniques reported in intervention studies of alcohol and tobacco use: a rapid review
Bibliographic record
Abstract
Background: Clinical guidelines recommend addressing alcohol and tobacco use simultaneously, but few providers offer brief alcohol interventions routinely, and these behaviours are often treated separately. While several interventions targeted dual use, there remains a gap in identifying behaviour change techniques (BCTs) designed to modify processes controlling dual use. Objective: To identify commonly used BCTs in interventions targeting both alcohol and tobacco use, their modes of delivery, and explore which BCTs are associated with smoking cessation and/or alcohol reduction. Methods: Following Cochrane recommendations, a rapid review to identify BCTs showing promise for reducing dual use was conducted. Using an eligibility criteria, we retrieved relevant papers from databases and used the Behavioural Change Taxonomy V1 tool to identify BCTs showing promise. Results: Thirty-eight articles of the initial systematic search of 2987 papers met the criteria for full article review. Goal setting, action planning, and pharmacological support were the most common BCTs identified. Most studies (33, 87%) had a low or moderate risk of bias. Of these 33 studies, 13 studies (39%) reported statistically significant outcomes of reduction or cessation in smoking behaviour and alcohol consumption. Face to face (25, 76%) was the most common intervention delivery method. Conclusion: Clinical trials identify goal setting, action planning and problem solving to address the dual use of tobacco and alcohol. Systematic reviews and meta-analyses are needed to evaluate the true impact of these programmes. Future studies should minimally include these BCTs and study the interactional effects of these BCTs on the efficacy of the intervention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".