Burnout in Italian hospital physicians during the COVID-19 pandemic: the roles of alexithymia and defense mechanisms
Bibliographic record
Abstract
Background: COVID-19 pandemic placed unusual additional burden upon international healthcare systems. This study aims to explore the associations between burnout, alexithymia and defense mechanisms in a group of Italian hospital physicians during the COVID-19 pandemic. Methods: 96 hospital physicians completed self-reported questionnaires through Google Forms platform, including Maslach Burnout Inventory (MBI), Defense Style Questionnaire-40 (DSQ-40), and 20-item Toronto Alexithymia Scale (TAS-20). Results: Emotional Exhaustion (EE) and Depersonalization (DP) burnout dimensions were positively correlated with alexithymia and with immature and neurotic defenses, while negative associations were correlated with a mature defensive style. MBI Personal Accomplishment (PA) was negatively correlated with alexithymia levels but positively correlated with mature defenses. According to regression models, EE levels were predicted by female gender (beta = −0.20; p .04) and DSQ mature defenses (beta= -.24; p .02); DP levels were predicted by alexithymia total score (beta= 0.26; p .04) and DSQ mature defenses (beta= -.20; p .05); and PA levels were predicted by alexithymia total score (beta = -0.29; p .02) and DSQ mature defense (beta= .45; p .001). Conclusions: Consistent with the broader literature, an association between burnout and both alexithymia and defense mechanisms emerged. These findings highlight the importance of reducing occupational-related burden on healthcare workers and of promoting protecting strategies to deal with emergency situations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".