Correlates of emotional intelligence among Lebanese adults: the role of depression, anxiety, suicidal ideation, alcohol use disorder, alexithymia and work fatigue
Bibliographic record
Abstract
BACKGROUND: Relationship between emotional intelligence and emotional variables such as stress, depression, anxiety and mental health has been well documented in child and adult samples. New insights into the association between emotional intelligence and different components of mental health in one study (cognitive, emotional and behavioral dimensions) can help patients, therapists, relatives, and friends to understand, explain, and cope with symptoms. There have been no studies assessing the association between the emotional intelligence (EI) with various factors in Lebanon. This study principal aim was to evaluate how EI is related to mental health issues: social anxiety, depression, alcohol use disorders (AUD), work fatigue, stress and alexithymia in Lebanon. METHODS: 789 participants were enrolled in a cross-sectional study between November 2017 and March 2018. A cluster analysis was used to evaluate participants' profiles with the help of emotional intelligence subscales, to separate the Lebanese population into equal limited units with different characteristics using the K-mean technique. RESULTS: Three clusters were computed dividing participants into low EI (cluster 1; 24.5%), moderate EI (cluster 2; 43.7%) and high EI (cluster 3; 31.7%) respectively. Fitting into the cluster 1 (low EI) was significantly associated with higher AUD, alexithymia, anxiety, depression, perceived stress, social phobia, emotional, mental and physical work fatigue, suicidal ideation compared to cluster 3 (high EI). Fitting into the cluster 2 (moderate EI) was significantly correlated with higher AUD, depression, alexithymia, anxiety, perceived stress, social phobia, mental work fatigue and suicidal ideation compared to cluster 3 (high EI). CONCLUSION: This study results suggest that emotional intelligence is related to different variables, warranting interventions to limit/decrease alcohol abuse and mental/psychological illnesses as much possible.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".