Factors associated with intentions to quit tobacco use in Lebanon: A cross-sectional survey
Bibliographic record
Abstract
Introduction: The prevalence of tobacco smoking in Lebanon is among the highest globally. This study aims to determine past attempts to quit smoking among adults and identify factors associated with intentions to quit. Methods: A nationally representative telephone survey was conducted between June and August 2022. Eligibility criteria included people aged >=18 years residing in Lebanon. The questionnaire was divided into three components: socio-demographic characteristics, cigarette and waterpipe tobacco use behaviours. Binary logistic regression was used to examine factors associated with intention to quit cigarette and waterpipe tobacco use. Results: A total of 2003 respondents were included in the study. The prevalence of any tobacco product use was 41%, the prevalence of current cigarette smoking was 41% and the prevalence of current waterpipe tobacco use was 20%. Approximately 24% of adults who smoke cigarettes and 26% of those who use waterpipe tobacco had previous quit attempts mainly due to health concerns. Intentions to quit smoking within the next 6 months were reported among 12% of survey respondents. Among adults who smoke, past quit attempts increased the likelihood of intentions to quit cigarette smoking by 5-fold (OR: 5.11; 95% CI: 1.80-14.47, p = 0.002) and waterpipe tobacco use by 7-fold (OR: 6.98, 95% CI: 2.63-18.51, p = <0.001). Age and income were associated with intentions to quit cigarette but not waterpipe tobacco use. Conclusion: Intention to quit smoking was strongly associated with past quitting attempts. Understanding factors associated with intentions to quit can help inform the development of context specific smoking cessation interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".