Alexithymia as a mediator and moderator of the relationship between occupational stress and burnout in healthcare workers
Bibliographic record
Abstract
Healthcare professionals frequently experience high occupational stress, which increases their risk of burnout. Alexithymia, marked by difficulty identifying and describing feelings and an externally oriented thinking style, may influence this link. However, its mediating and moderating roles in the stress–burnout relationship remain underexplored in medical professionals. A cross-sectional study was conducted with 120 healthcare professionals aged 35–45 from four medical specialties in eastern Uttar Pradesh, India. Using stratified sampling to ensure equal representation, participants completed standardized behavioral tools, including Hindi versions of the Toronto Alexithymia Scale (TAS-20), the Medical Professionals’ Work-Related Stress Inventory, and the Maslach Burnout Inventory. Statistical analyses included bivariate correlations, stepwise multiple regression, and moderated mediation analysis to examine relationships among occupational stress, alexithymia dimensions, and burnout components. Occupational stress was positively associated with emotional exhaustion and depersonalization, and negatively associated with personal accomplishment. DDF was the strongest predictor of emotional exhaustion and depersonalization, while EOT forecasted low personal achievement. Mediation analyses revealed that DIF and DDF partly mediated the relationship between stress and burnout. DDF also moderated the connection between stress and emotional exhaustion, with the indirect effect of stress through DDF being more pronounced for individuals with higher DIF. Healthcare workers who struggle to identify and express emotions are particularly susceptible to stress-related burnout. Interventions aimed at enhancing emotional awareness and expression, alongside strategies to manage occupational stress, may help reduce burnout in high-demand clinical settings.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".