Stigma, Perceived Discrimination, and Mental Health during China’s COVID-19 Outbreak: A Mixed-Methods Investigation
Bibliographic record
Abstract
Research on stigma and discrimination during COVID-19 has focused on racism and xenophobia in Western countries. In comparison, little research has considered stigma processes, discrimination, and their public health implications in non-Western contexts. This study draws on quantitative survey data (N = 7,942) and qualitative interview data (N = 50) to understand the emergence, experiences, and mental health implications of stigma and discrimination during China’s COVID-19 outbreak. Given China’s history of regionalism, we theorize and use a survey experiment to empirically assess region-based stigma: People who lived in Hubei (the hardest hit province) during the outbreak and those who were socially associated with Hubei were stigmatized. Furthermore, the COVID-19 outbreak created stigma around people labeled as patients by the state. These stigmatized groups reported greater perceived discrimination, which—as a stressor—led to psychological distress. Our interview data illuminated how the stigmatized groups perceived, experienced, and coped with discrimination and stigma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".