Investigating the Relationship Between Corona Anxiety and Nursing Care Behaviors Working in Corona's Referral Hospitals
Bibliographic record
Abstract
Objectives: The prevalence of Coronavirus and its health-related psychosocial consequences is one of the most important human social events of the 21st century. Nurses, due to close contact with patients, are vulnerable to be infected with Covid-19. Therefore, they face severe psychological consequences. This study aimed to determine the relationship between Corona’s anxiety and nursing care behaviors in working in Corona referral hospitals in Kerman in 2020. Methods: The present study is cross-sectional descriptive-correlational research. Sampling was performed by the census method. A total of 166 nurses entered the study. In the present study, three demographic questionnaires, the Corona Disease Anxiety Scale (CDAS) and Caring Behaviors Inventory (CBI) were used. The analysis was done using Descriptive and Inferential statistics SPSS V. 18 software Results: The overall score of Corona anxiety among the nurses was 21.39±9.8, and the overall score of the nursing behavior of the studied nurses was 109.7±4.2 with a range of 94 to 118. Spearman’s correlation coefficient showed that there was no significant relationship between corona anxiety and caring behaviors. Conclusion: The present study showed that nurses working in corona wards suffer from moderate anxiety, and the level of caring behaviors provided by nurses was optimal. According to the current study findings, it is suggested that during the outbreak of emerging and epidemic diseases, to reduce nursing staff’s anxiety, coping strategies and resilience skills, and problem-solving, managers should pay more attention.
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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 | 0.004 |
| 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".