Association between alexithymia, emotional intelligence, smoking addiction, and alcohol use disorder among a sample of Lebanese adults
Bibliographic record
Abstract
BACKGROUND: Alexithymia can be associated with worse addictive traits, while emotional intelligence is associated with better addictive outcomes. In Lebanon, the prevalence of cigarette and waterpipe smoking is on the rise, although people are aware of the associated harms. Also, around 11% of Lebanese adults have experienced alcohol use disorder (AUD). This study aimed to assess the association between alexithymia, emotional intelligence, smoking (cigarette and waterpipe), and AUD among a sample of Lebanese adults. METHODS: A web-based cross-sectional study carried out between February and April 2020, during the lockdown period, enrolled 408 community-dwelling adults. The survey link was shared on social media to reach participants from all Lebanese districts/governorates. RESULTS: Taking antidepressants (Beta = 4.37) was significantly associated with more cigarette dependence, while female gender (Beta = -1.52) and having a high vs. low monthly income (Beta = 1.02) were significantly associated with less cigarette dependence. None of the variables, including alexithymia, were significantly associated with waterpipe dependence. Female gender (Beta = -0.15) and higher emotional intelligence (Beta = -0.003) were significantly associated with less AUD, whereas higher alexithymia (Beta = 0.003) was significantly associated with more AUD. CONCLUSION: This study could demonstrate a significant association between alexithymia and cigarette smoking and alexithymia and alcohol consumption. Future research is warranted to investigate the mediating effect of emotional intelligence and how these results may be used to meet the needs of alexithymic individuals with addictions.
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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 | 0.000 | 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 | 0.000 | 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".