Burnout and Its Relationships With Alexithymia, Stress, Self-Esteem, Depression, Alcohol Use Disorders, and Emotional Intelligence
Bibliographic record
Abstract
Our aim was to assess the relationship between personality and psychological traits, and burnout among the Lebanese population. A questionnaire-based cross-sectional study was conducted with multiple validated scales used to measure burnout and other characteristics. A cluster analysis was then performed to split the population into mutually exclusive groups with different profiles according to the burnout scales using the K-mean method. A multivariate analysis of covariance was carried out to compare multiple measures between the cluster groups under comparison. The study, conducted between November 2017 and March 2018, enrolled 789 participants. The results showed that 100 (14.0%) had high emotional work fatigue, whereas 443 (62.5%) and 680 (95.4%) had high mental and physical work fatigue, respectively. People with high physical work fatigue (cluster 1) had lower alcohol dependence (β = -2.78), alexithymia (β = -3.16), depression (β = -7.20), anxiety (β = -6.99), perceived stress (β = -2.53), social phobia (β = -11.49), suicidal ideation (β = -0.35), emotional awareness (β = -4.54), emotional managament (β = -1.71), social emotional awareness (β = -9.27), and relationship management (β = -9.12). People with high emotional work fatigue (cluster 2) had higher alcohol dependence (β = 2.11), alexithymia (β = 6.51), depression (β = 2.48), anxiety (β = 4.11), perceived stress (β = 4.30), and lower emotional awareness (β = -6.68), emotional management (β = -7.80), social emotional awareness (β = -3.71), and relationship management (β = -3.05). Higher levels of burnout were found to be associated with multiple psychological factors. The results would help understand the burnout dimensions and their correlated factors in the Lebanese population.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".