The Correlation between Nurses’ COVID-19 Infections and Their Emotional State and Work Conditions during the SARS-CoV-2 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic caused by the SARS-CoV-2 virus has significantly influenced the functioning of Polish hospitals, and thus, the working conditions of nurses. Research on the presence of specific negative emotions in nurses may help identify deficits in the future, as well as directing preventive actions. The present research was performed among nurses (n = 158) working in Polish healthcare facilities during the third wave of the COVID-19 pandemic caused by the SARS-CoV-2 virus, where Group A (n = 79) consisted of nurses diagnosed with COVID-19, and Group B (n = 79) nurses who have never been infected with COVID-19. To perform the research, the Courtauld Emotional Control Scale (CECS), Trait Anxiety Scale (Polish: SL-C) and the authors’ survey questionnaire were used. A positive test result was generally determined more often among nurses who indicated a noninfectious ward as their main workplace, compared to nurses employed in infectious wards (64.55% positive vs 33.45% negative). Over a half of the subjects identified moderate levels of emotion suppression as the method to regulate strong emotions, while one-quarter cited high levels of suppression. Anxiety was suppressed at high and moderate levels by 97% of the subjects, depression by 86.71%, and anger by 79.48%. Infection with COVID-19 results in a higher level of anxiety and depression, as well as a feeling of increased work load.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".