Relationship between Stress and Alexithymia, Emotional Processing and Negative/Positive Affect in Medical Staff Working amid the COVID-19 Pandemic
Bibliographic record
Abstract
The psychological burden of the COVID-19 pandemic may have a lasting effect on emotional well-being of healthcare workers. Medical personnel working at the time of the pandemic may experience elevated occupational stress due to the uncontrollability of the virus, high perceived risk of infection, poor understanding of the novel virus transmission routes and unavailability of effective antiviral agents. This study used path analysis to analyze the relationship between stress and alexithymia, emotional processing and negative/positive affect in healthcare workers. The sample included 167 nurses, 65 physicians and 53 paramedics. Sixty-two (21.75 %) respondents worked in COVID-19-designated hospitals. Respondents were administered the Toronto Alexithymia Scale-20, Cohen's Perceived Stress Scale, Emotional Processing Scale, and the Positive and Negative Affect Schedule. The model showed excellent fit indices (χ 2 (2)=2.642, p=0.267; CFI=0.999, RMSEA=0.034, SRMR=0.015). Multiple group path analysis demonstrated physicians differed from nurses and paramedics at the model level (X 2 diff (7)=14.155, p<0.05 and X 2 diff (7)=18.642, p<0.01, respectively). The relationship between alexithymia and emotional processing was stronger in nurses than in physicians (difference in beta=0.27; p<0.05). Individual path χ 2 tests also revealed significantly different paths across these groups. The results of the study may be used to develop evidence-based intervention programs promoting healthcare workers’ mental health and well-being.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| 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".