Alexithymia, traumatic stress symptoms and burnout in female healthcare professionals
Bibliographic record
Abstract
Objective The burnout syndrome represents a defence mechanism against stress and includes stages with decreased ability to experience feelings and emotional states. This finding suggests that burnout might be closely linked to emotional ‘blindness’ as a defence mechanism against negative and overwhelming emotions known as alexithymia. The aim of this study is to examine the relationships between burnout syndrome, alexithymia, depression and traumatic stress symptoms in healthcare professionals. Methods This empirical study assessed female healthcare professionals who work with a population of patients with diabetes, utilizing the Maslach Burnout Inventory (MBI-HSSMP), Burnout Measure (BM), Toronto Alexithymia Scale (TAS-20), Beck Depression Inventory (BDI-II) and Traumatic Stress Checklist (TSC-40). Data were analysed using Spearman’s correlation coefficient. Results A total of 114 female participants were included (age range, 31–60 years; mean age, 46.62 ± 8.71 years). Statistically significant associations were found between burnout syndrome (BM scores) and alexithymia (TAS-20) ( r = 0.41), and between BM scores and traumatic stress (TSC-40; r = 0.63). The MBI-HSSMP emotional exhaustion subscale also correlated with alexithymia (TAS-20) ( r = 0.37). Conclusion Findings of this study suggest that alexithymia and traumatic stress are related to burnout symptoms. This dynamic may be potentially useful for detecting and preventing burnout syndrome.
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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 | 0.003 |
| 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 | 0.000 | 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".