The Mental Health Implications of a Local Epidemic: Early Experience with the COVID-19 Outbreak in Wuhan, China
Notice bibliographique
Résumé
Purpose: This article presents national survey data related to the mental health of participants (N = 13,824) from every province in the People’s Republic of China, in the period of early February, during the early days of the outbreak of the COVID-19 virus. The goal was to examine stress and anxiety levels, even as the virus was spreading across the country. This goal was facilitated by data collected specifically from Wuhan city, which was the geographical epicenter of the outbreak, Hubei, the province in which Wuhan is located, and the rest of China. Methods: The survey included a series of validated measures of mental health, as well as measured constructs such as stress and anxiety, depression, sleep, workplace wellness and social cohesion. To the extent possible, the measures had been previously validated in China and were available in Simplified Chinese, although some measures were created for the unique characteristics of the viral outbreak. The survey was distributed electronically through a network of researchers. Results: The measures were generally reliable, with expected inter-correlations. The factor structure of the Symptom Checklist-90 items was generally consistent with its conceptual dimensions. Mostly importantly, the study revealed a gradient of mental health outcomes, with participants from the city of Wuhan having the highest scores on most mental health measures, with the great Hubei province similar or slightly lower, and the rest of China having elevated, but lower scores on most outcomes. Sleep disturbance also revealed a similar gradient, with participants in the epicenter reporting the most sleep disturbance. Discussion: These results reveal the ability to capture the mental health of citizens during a viral outbreak, and the sensitivity of measures to the mental challenges such an outbreak brings. The gradient of responses revealed the spreading effect of the COVID-19 outbreak, and suggests that worse mental health is highly likely to be an outcome as a pandemic spreads. These results suggest that health officials need to attend to not only the physical and direct effects of a viral pandemic, but also to the importance of the mental health of their citizens.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».