Alexithymia, Burnout, and Hopelessness in a Large Sample of Healthcare Workers during the Third Wave of COVID-19 in Italy
Bibliographic record
Abstract
In the present study, we aimed to assess the frequency of and the relationships between alexithymia, burnout, and hopelessness in a large sample of healthcare workers (HCWs) during the third wave of COVID-19 in Italy. Alexithymia was evaluated by the Italian version of the 20-item Toronto Alexithymia Scale (TAS-20) and its subscales Difficulty in Identifying Feelings (DIF), Difficulty in Describing Feelings (DDF), and Externally Oriented Thinking (EOT), burnout was measured with the scales emotional exhaustion (EE), depersonalisation (DP), and personal accomplishment (PA) of the Maslach Burnout Test (MBI), hopelessness was measured using the Beck Hopelessness Scale (BHS), and irritability (IRR), depression (DEP), and anxiety (ANX) were evaluated with the Italian version of the Irritability‚ Depression‚ Anxiety Scale (IDA). This cross-sectional study recruited a sample of 1445 HCWs from a large urban healthcare facility in Italy from 1 May to 31 June 2021. The comparison between individuals that were positive (n = 214, 14.8%) or not for alexithymia (n = 1231, 85.2%), controlling for age, gender, and working seniority, revealed that positive subjects showed higher scores on BHS, EE, DP IRR, DEP, ANX, DIF, DDF, and EOT and lower on PA than the not positive ones (p < 0.001). In the linear regression model, higher working seniority as well as higher EE, IRR, DEP, ANX, and DDF scores and lower PA were associated with higher hopelessness. In conclusion, increased hopelessness was associated with higher burnout and alexithymia. Comprehensive strategies should be implemented to support HCWs’ mental health and mitigate the negative consequences of alexithymia, burnout, and hopelessness.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".