Alcohol and Sedative Use Disorders in the Lebanese Population: Role of Sleep and Psychiatric Factors
Bibliographic record
Abstract
Introduction Alcohol and sedative substance use disorders are escalating global public health challenges. Lebanon has grappled with multiple crises, including economic, healthcare, and social issues. Objectives This study aimed to assess the correlates of the alcohol and sedative substance use risk scores with sociodemographic and clinical factors, including sleep disorders, chronotype, anxiety, and depression. Methods A cross-sectional study was conducted among the Lebanese population using several validated scales to assess the risk of alcohol and sedative substance use, including the Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST). Other tools evaluated chronotype, sleep, and mood disturbances. Bivariate and multivariable analyses were then performed, taking the alcohol and sedative scores as dependent variables. Results A total of 646 participants were included. Multivariate analysis revealed positive and significant correlations between higher ASSIST-alcohol scores and personal history of alcohol abuse (B=4.61), family history of prescription substance abuse (B=1.763), psychiatric disorders (B=2.898), and worse Insomnia Severity Index scores (Beta=0.14). Conversely, ASSIST-alcohol scores negatively correlated with weight (B= -0.39) and morning chronotype (B=-0.084). Positive correlations were identified between higher ASSIST-alcohol scores and personal history of illicit substance abuse (B=2.834), prescription substance abuse (B=2.252), sleep quality (B=0.130), and sleep severity (B=0.082), while negatively correlating with cigarette smoking (B=-0.038). Conclusions This study elucidates the role of several predisposing factors to alcohol and sedative use disorders in Lebanon, including history of substance abuse, psychiatric disorders, sleep disorders, and chronotype. These findings advocate, in particular, for the integration of sleep disorder assessment and management into addiction rehabilitation programs. Disclosure of Interest None Declared
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".