Mental Health, Risk Factors, and Social Media Use During the COVID-19 Epidemic and Cordon Sanitaire Among the Community and Health Professionals in Wuhan, China: Cross-Sectional Survey
Bibliographic record
Abstract
BACKGROUND: The mental health consequences of the coronavirus disease (COVID-19) pandemic, community-wide interventions, and social media use during a pandemic are unclear. The first and most draconian interventions have been implemented in Wuhan, China, and these countermeasures have been increasingly deployed by countries around the world. OBJECTIVE: The aim of this study was to examine risk factors, including the use of social media, for probable anxiety and depression in the community and among health professionals in the epicenter, Wuhan, China. METHODS: We conducted an online survey via WeChat, the most widely used social media platform in China, which was administered to 1577 community-based adults and 214 health professionals in Wuhan. Probable anxiety and probable depression were assessed by the validated Generalized Anxiety Disorder-2 (cutoff ≥3) and Patient Health Questionnaire-2 (cutoff ≥3), respectively. A multivariable logistic regression analysis was used to examine factors associated with probable anxiety and probable depression. RESULTS: Of the 1577 community-based adults, about one-fifth of respondents reported probable anxiety (n=376, 23.84%, 95% CI 21.8-26.0) and probable depression (n=303, 19.21%, 95% CI 17.3-21.2). Similarly, of the 214 health professionals, about one-fifth of surveyed health professionals reported probable anxiety (n=47, 22.0%, 95% CI 16.6-28.1) or probable depression (n=41, 19.2%, 95% CI 14.1-25.1). Around one-third of community-based adults and health professionals spent ≥2 hours daily on COVID-19 news via social media. Close contact with individuals with COVID-19 and spending ≥2 hours daily on COVID-19 news via social media were associated with probable anxiety and depression in community-based adults. Social support was associated with less probable anxiety and depression in both health professionals and community-based adults. CONCLUSIONS: The internet could be harnessed for telemedicine and restoring daily routines, yet caution is warranted toward spending excessive time searching for COVID-19 news on social media given the infodemic and emotional contagion through online social networks. Online platforms may be used to monitor the toll of the pandemic on mental health.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".