The Mental Health Implications of a Local Epidemic: Early Experience with the COVID-19 Outbreak in Wuhan, China
Bibliographic record
Abstract
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".