Factors associated with changes in inpatients’ smoking pattern during hospitalization and one month after discharge: A cohort study
Bibliographic record
Abstract
INTRODUCTION: Smokers are frequent users of healthcare services. Admissions to hospital can serve as a "teachable moment" for quitting smoking. Clinical guidelines recommend initiating smoking cessation services during hospitalization; however, in Southern European countries less than 5% of inpatients receive a brief intervention for smoking cessation. OBJECTIVES: The aims of this study were (i) to examine rates of smoking abstinence during and after hospitalization; (ii) to measure changes in smoking patterns among persons who continued smoking after discharge; and (iii) to identify predictors of abstinence during hospitalization and after discharge. METHODS: A cohort study of a representative sample of current adult smokers hospitalized in two Spanish and two Portuguese hospitals. We surveyed smokers during hospitalization and recontacted them one month after discharge. We used a 25-item ad hoc questionnaire regarding their smoking pattern, the smoking cessation intervention they have received during hospitalization, and hospital and sociodemographic characteristics. We performed a descriptive analysis using the chi-square test and a multivariate logistic regression to characterize the participant, hospital, and smoking cessation intervention (5As model) characteristics associated with smoking abstinence. RESULTS: Smoking patients from both countries presented high abstinence rates during hospitalization (Spain: 76.4%; Portugal: 70.2%); however, after discharge, their abstinence rates decreased to 55.3% and 46.8%, respectively. In Spain, smokers who tried to quit before hospital admission showed higher abstinence rates, and those who continued smoking reduced a mean of five cigarettes the number of cigarettes per day (p ≤ 0.001). In Portugal, abstinence rates were higher among women (p = 0.030), those not living with a smoker (p = 0.008), those admitted to medical-surgical wards (p = 0.035), who consumed their first cigarette within 60 min after waking (p = 0.006), and those who were trying to quit before hospitalization (p = 0.043). CONCLUSIONS: Half of the smokers admitted into the Spanish hospitals are abstinent one month after discharge or have reduced their cigarettes per day. Nevertheless, success rates could be increased by implementing evidence-based tobacco cessation programs at the organizational-level, including post-discharge active quitting smoking support. CLINICAL RELEVANCE: Three-quarters of the inpatients who smoke remain abstinent during hospitalization and over half achieve to maintain their abstinence or at least reduce their consumption one month after discharge, proving that admission to hospitals is an excellent teachable moment to quit smoking.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".