Smoking during and 1-month after discharge in Southern European countries (Spain and Portugal)
Bibliographic record
Abstract
Introduction Smoking prevalence is still high in Southern European countries. Smokers are frequent users of hospitals and hospital admission might be an adequate moment for quitting. The aim of this study was to assess changes in smoking status, willingness to quit, and quit attempts among current smokers during hospitalization and one-month after discharge. Methods We conducted a survey among current smokers hospitalized in two convenience hospitals in Portugal and two in Spain during hospitalization and one-month after discharge. A representative sample of conscious and oriented smokers participated after giving their informant consent and telephone number. The survey included questions about patients’ smoking status (abstinence, cigarettes per day, etc), their willingness to quit smoking, quit attempts, and other socio-economic variables. We conducted a Chi-squared bivariate descriptive analysis, stratified by country. Results 211 smokers were identified during hospitalization (58 from Portugal and 153 from Spain). Overall 74% of smokers were abstinent during hospitalization. Women, ≥55 years/old, and those who live with a non-smoker presented a higher percentage of abstinence than their comparisons (men, 0.001). The main reason to continue smoking was nicotine dependence and anxiety; while the main reasons to quit smoking were receiving health professional advice and personal decision (42.8% in both cases). Conclusion Hospitalization is a key moment to promote smoking abstinence and quit attempts. Our findings suggest the need to promote smoking cessation during and after hospital stay.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".