Knowledge, Risk Perceptions and Depression Related to COVID-19: The Comparison between Nurses and other Professionals in Nanjing, China
Bibliographic record
Abstract
Background: COVID-19 is a deadly infectious disease that dramatically affects the safety of hospital professionals. Their knowledge, risk perception, and depression levels towards COVID-19 need to be understood.
 Purpose: This study aimed to compare the differences in knowledge, risk perceptions, and depression related to COVID-19 between nurses and other professionals in hospital settings.
 Methods: A cross-sectional survey was conducted in Nanjing, China at the beginning of the COVID-19 pandemic with four standardized questionnaires, including (a) demographic data, (b) knowledge about COVID-19, (c) risk perceptions, and (d) depression. Data from the two groups of participants were analyzed by Chi-square tests, correlations, and t-tests.
 Results: The mean correct answer rate of knowledge for nurses was 76.42%, and for other professionals was 73.94%. T-tests indicated significant differences in total mean knowledge score and mean scores in four out of five subscale scores (p<.05). All significant differences in scores showed that nurses' knowledge was higher than other professionals, except one subscale score, which revealed that nurses' knowledge of pets could spread COVID-19 was lower than other professionals. The highest perceived risk scores in both groups were contracting influenza. The second highest was scores on COVID-19 and H1N 1 the third. T-tests indicated significant differences between these two groups in scores of contracting these three infectious diseases, with nurses higher than other professionals (p<.001). T-test also showed that the depression of nurses was higher than other professionals (p<.000). Positive relationships existed between risk perceptions and depression (p<.001).
 Conclusions: More education is needed to improve hospital professionals' knowledge of COVID-19. Since nurses' risk perceptions of contracting COVID-19 and dying from this deadly infection were higher than other professionals; further studies might help researchers understand the underlying reasons better. Hospital leaders should pay attention to workers' mental health and initiate proper strategies to reduce their depression related to COVID-19. Further investigation is needed since few publications mention the relationship between the perceived risk of hospital professionals and home and food accidents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".