The Early 2020 COVID-19 Outbreak in China and Subsequent Flourishing: Medium-Term Effects and Intervening Mechanisms
Bibliographic record
Abstract
In early 2020, a COVID-19 outbreak occurred in Hubei Province of China. Exploiting the geographic concentration of China’s COVID-19 cases in Hubei (the initial epicenter), we compare Hubei and non-Hubei residents to examine the medium-term effect of exposure to the COVID-19 outbreak on mental well-being. We examine flourishing—a comprehensive assessment of well-being that is not merely the absence of mental illness—and investigate a broad set of psychosocial and economic mediators that may link initial outbreak exposure to subsequent flourishing. We use ordinary least squares regression models to analyze national panel data collected in early 2020 and late 2021 ( N = 3,169). Results show that flourishing scores remain lower for Hubei than non-Hubei residents almost two years following the early 2020 COVID-19 outbreak. Mediation analysis reveals that Hubei residents’ lower incidences of job promotion and lower sense of control are the two most important mediators accounting for their lower flourishing relative to non-Hubei residents. Combined, this study provides the first evidence of the medium-term psychological vulnerability borne by individuals who lived in the initial epicenter of the COVID-19 pandemic. Findings on the intervening mechanisms shed light on the policy initiatives needed for post-pandemic mental well-being recovery in China and other countries.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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".