THE EFFECT OF THE COVID-19 CRISIS ON THE SATISFACTION AND PERFORMANCE OF NURSES
Bibliographic record
Abstract
The study reviews the effect of the COVID-19 crisis on the satisfaction and performance of nurses during the crisis as examined in studies carried out in ten countries (Italy, Australia, Great Britain, Germany, Israel, New Zealand, Spain, Canada, Romania, and Switzerland). The purpose of the study is to identify the scope and frequency of the phenomena reported by the nursing workforce from a broad global perspective and to provide information and recommendations to improve preparation for the future. The review shows that the COVID-19 crisis has affected the mental health of the nurses (Spain, Switzerland), anxiety, depression, and burnout (Switzerland, New Zealand, Canada, Germany), deterioration of working conditions mainly due to lack of staff, high employee turnover, and the lack of personal protective equipment (UK), and symptoms of stress and fear of contracting the disease (Romania, Germany, Canada). Also, the experience of coping during the pandemic period is seen as a deep mental trauma (Italy) and is characterized by the lack of mental support for the medical staff (Israel). The phenomena reported by the nursing staff in the countries examined caused less ability to function, lack of confidence, (to plan, concentrate, and organize), and a general decrease in motivation (Australia). Post-traumatic stress disorders were reported in all the countries examined.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".