Knowledge, Risk Perceptions and Depression Related to COVID-19: The Comparison between Nurses and other Professionals in Nanjing, China
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».