Identifying contexts and mechanisms in multiple behavior change interventions affecting smoking cessation success: a rapid realist review
Bibliographic record
Abstract
BACKGROUND: Smoking continues to be a leading cause of preventable chronic disease-related morbidity and mortality, excess healthcare expenditure, and lost work productivity. Tobacco users are disproportionately more likely to be engaging in other modifiable risk behaviours such as excess alcohol consumption, physical inactivity, and poor diet. While hundreds of interventions addressing the clustering of smoking and other modifiable risk behaviours have been conducted worldwide, there is insufficient information available about the context and mechanisms in these interventions that promote successful smoking cessation. The aim of this rapid realist review was to identify possible contexts and mechanisms used in multiple health behaviour change interventions (targeting tobacco and two or more additional risk behaviours) that are associated with improving smoking cessation outcome. METHODS: This realist review method incorporated the following steps: (1) clarifying the scope, (2) searching for relevant evidence, (3) relevance confirmation, data extraction, and quality assessment, (4) data analysis and synthesis. RESULTS: Of the 20,423 articles screened, 138 articles were included in this realist review. Following Michie et al.'s behavior change model (the COM-B model), capability, opportunity, and motivation were used to identify the mechanisms of behaviour change. Universally, increasing opportunities (i.e. factors that lie outside the individual that prompt the behaviour or make it possible) for participants to engage in healthy behaviours was associated with smoking cessation success. However, increasing participant's capability or motivation to make a behaviour change was only successful within certain contexts. CONCLUSION: In order to address multiple health behaviours and assist individuals in quitting smoking, public health promotion interventions need to shift away from 'individualistic epidemiology' and invest resources into modifying factors that are external from the individual (i.e. creating a supportive environment). TRIAL REGISTRATION: PROSPERO registration number: CRD42017064430.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".