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 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 | 0.001 | 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".