Barriers and facilitators to perioperative smoking cessation: A scoping review
Bibliographic record
Abstract
OBJECTIVE: Smoking cessation interventions are underutilized in the surgical setting. We aimed to systematically identify the barriers and facilitators to smoking cessation in the surgical setting. METHODS: Following the Joanna Briggs Institute (JBI) framework for scoping reviews, we searched 5 databases (MEDLINE, Embase, Cochrane CENTRAL, CINAHL, and PsycINFO) for quantitative or qualitative studies published in English (since 2000) evaluating barriers and facilitators to perioperative smoking cessation interventions. Data were analyzed using thematic analysis and mapped to the theoretical domains framework (TDF). RESULTS: From 31 studies, we identified 23 unique barriers and 13 facilitators mapped to 11 of the 14 TDF domains. The barriers were within the domains of knowledge (e.g., inadequate knowledge of smoking cessation interventions) in 23 (74.2%) studies; environmental context and resources (e.g., lack of time to deliver smoking cessation interventions) in 19 (61.3%) studies; beliefs about capabilities (e.g., belief that patients are nervous about surgery/diagnosis) in 14 (45.2%) studies; and social/professional role and identity (e.g., surgeons do not believe it is their role to provide smoking cessation interventions) in 8 (25.8%) studies. Facilitators were mainly within the domains of environmental context and resources (e.g., provision of quit smoking advice as routine surgical care) in 15 (48.4%) studies, reinforcement (e.g., surgery itself as a motivator to kickstart quit attempts) in 8 (25.8%) studies, and skills (e.g., smoking cessation training and awareness of guidelines) in 5 (16.2%) studies. CONCLUSION: The identified barriers and facilitators are actionable targets for future studies aimed at translating evidence informed smoking cessation interventions into practice in perioperative settings. More research is needed to evaluate how targeting these barriers and facilitators will impact smoking outcomes.
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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 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".