Alexithymia and its association with burnout, depression and family support among Greek nursing staff
Bibliographic record
Abstract
BACKGROUND: Few studies have examined the relation between alexithymia (i.e. the inability to recognize and verbalize emotions) and professional burnout. Considering the absence of relevant studies in the Greek scientific literature, the aim of this work was to examine the associations of alexithymia with the three facets of professional burnout, the perception of family support and depression in nursing personnel. METHODS: The study was performed in one of the largest hospitals in Greece and included 95 nurses. Assessments of alexithymia, burnout, depression and family support were made by means of the Toronto Alexithymia Scale, the Maslach Burnout Inventory, the Beck Depression Inventory and the Julkunen Family Support Scale, respectively. Student's t-test, Pearson's correlation and stepwise linear regression were used for the evaluation of data. RESULTS: Alexithymia was correlated positively with depression, emotional exhaustion and depersonalization, and negatively with sense of family support and personal achievement. Additionally, family support was correlated positively with personal achievement and negatively with depression. CONCLUSION: In the scientific literature there is a debate as to whether alexithymia is a stable personality characteristic or if it is dependent on symptoms of mental disorders. We tried to interpret the associations of alexithymia with professional burnout, depressive symptoms and family support. From this study it appears very likely that alexithymia is directly associated with depression and personal achievement, but also indirectly with the sense of family support.
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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 | 0.000 |
| 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) | 0.001 | 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".