Work-Related Traumatic Stress Response in Nurses Employed in COVID-19 Settings
Bibliographic record
Abstract
Nurses may be at a higher risk of experiencing work-related traumatic stress response during the COVID-19 pandemic compared to other clinicians. This study aimed to investigate the correlations between work-related trauma symptoms and demographic factors, psychosocial hazards and stress response in a census sample of nurses working in COVID-19 settings in Cyprus. In this nationwide descriptive and cross-sectional study, data were collected between April and May 2020 using a questionnaire that included sociodemographic, educational and employment and work-related variables, as well as a modified version of the Secondary Traumatic Stress Scale (STSS) for the assessment of work-related trauma symptoms during the pandemic. Overall, 233 nurses participated (with a response rate of 61.3%) and 25.7% of them reported clinical work-related trauma symptoms (STSS-M > 55; actual scale range: 17–85). The mean value for emotional exhaustion was 7.3 (SD: 2.29; visual scale range: 1–10), while the value for distress that was caused by being avoided due to work in COVID-19 units was 6.98 (SD: 2.69; visual scale range: 1–10). Positive associations were noted between trauma symptoms and both emotional exhaustion and distress from being avoided by others due to work in a COVID-19 setting and a negative association was also found between trauma symptoms and satisfaction from organizational support variables (all p < 0.002). Working in COVID-19 settings during the pandemic is a stressful experience that has been linked to psychologically traumatic symptoms Thus, supportive measures are proposed for healthcare personnel, even in countries with low COVID-19 burden.
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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 | 0.002 |
| 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) | 0.002 | 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".