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.
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 | 0.033 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.026 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".