Knowledge and Psychological Stress Related to COVID-19 Among Nursing Staff in a Hospital in China: Cross-Sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: Since December 2019, coronavirus disease (COVID-19) has been rapidly spreading worldwide. Nurses play a key role in fighting this disease and are at risk of COVID-19 infection. Therefore, there is an urgent need to assess the mental health condition of nurses and establish appropriate interventions to reduce the negative psychiatric outcomes of the pandemic. OBJECTIVE: The objectives of this study were to evaluate the knowledge and psychological stress related to COVID-19 among nursing staff and to provide evidence of the need for targeted training and psychological intervention. METHODS: This cross-sectional web-based survey study was performed in a class 3 grade A general hospital in a southwest province of China from March 1 to March 15, 2020. A self-designed questionnaire with questions about COVID-19-related prevention and control knowledge and the Triage Assessment Form (TAF) were used to assess nursing staff's knowledge of COVID-19 and their degree of psychological stress, respectively. SPSS 23.0 was applied for statistical analysis of the collected data. RESULTS: A total of 979 nurses completed the questionnaire. The results showed that the nursing staff provided the fewest correct answers to questions about continuous viral nucleic acid testing specifications (379/979 correct answers, 38.7%), isolation/discharge criteria (539/979 correct answers, 55.1%), and management measures for patients with suspected symptoms (713/979 correct answers, 72.8%). The median total score of the TAF was 7.0 (IQR 5.0-12.0), and there were statistically significant differences in scores between different nursing roles, years of work experience, and hospital departments (P<.05). CONCLUSIONS: This study indicated that nursing staff have insufficient knowledge about COVID-19. Meanwhile, although the psychological damage to nurses during the pandemic was found to be low, nurse managers must continue to monitor the mental health of nursing staff and perform timely interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".