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 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) | 0.008 | 0.004 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".