Smoking cessation interventions on health-care workers: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: The authors carried out a systematic review and a meta-analysis on smoking cessation interventions on health -care workers to clarify the state of the art interventions and to identify the best one. MATERIALS AND METHODS: This review was registered with PROSPERO: CRD42019130117. The databases PubMed, Scopus, Web of Science and CINAHL were searched until December 2018. Quality of all studies included in the systematic review was assessed according to the Newcastle-Ottawa Scale (NOS) on cohort or cross-sectional studies and to the Cochrane Risk of Bias Tool for Randomized Controlled Trials. Meta-analysis and meta-regression analyses were also carried out for cohort studies (quasi-experimental or a before-after studies design) and clinical trials. RESULTS: for homogeneity <0.01), but they have all shown positive results since they reached the goal of smoking cessation among health-care workers, even if with different proportions. Meta-analysis was performed on 10 studies (six cohort studies and four clinical trials), showing a 21% of success rate from the application of smoking cessation interventions, either pharmacological or behavioral ones. The resulted pooled RR (Risk Ratio) was 1.21 (95% CI [1.06-1.38]), being 24% of success rate from clinical trials (pooled RR 1.244; 95% CI [1.099-1.407]) and 19% of success rate from cohort studies (pooled RR 1.192; 0.996-1.426). However, two studies have confidence intervals which include unity and one study has a wide confidence interval; as a consequence, the meta-analysis for its results depends heavily on one single study. Meta-regression analysis revealed that results were influenced by the number of participants. CONCLUSION: Both policy and pharmaceutical interventions can obtain positive results in quitting smoking among health-care workers. However, as shown by our review, combination approaches can produce better results in terms of cessation percentages and 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 | 0.023 | 0.045 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.030 | 0.057 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".