COVID‐19‐related stigma and its impact on psychological distress: A cross‐sectional study in Wuhan, China
Bibliographic record
Abstract
Abstract Background and Aims Health‐related stigma arises from the perceived association between a person or group of certain characteristics and a specific disease. Coronavirus disease 2019 (COVID‐19) has brought about stigma targeted at individuals and groups who are perceived to be connected with the virus. Wuhan of China was not only the locale where the first COVID‐19 cases were detected in the world but was also the hardest hit across China. Methods Using new data (N = 1153) from a survey conducted in Wuhan in August 2020, this cross‐sectional study aims to reveal the stigma experienced by residents in Wuhan during the COVID‐19 pandemic and the impact of this experienced stigma on psychological distress, specifically posttraumatic stress disorder. Results 69.47% (95% confidence interval (CI): 66.81%─72.13%) of the surveyed Wuhan residents have experienced some forms of stigma related to COVID‐19. The average posttraumatic stress disorder score based on the impact of event scale–revised is 20.28 (95% CI: 19.096─21.468) out of 88. In particular, 27.75% (95% CI: 25.17%─30.34%) of the respondents display clinically significant distress symptoms. Moreover, this stigma not only aggravates individuals' posttraumatic stress disorder score by 10.652 (95% CI: 8.163─13.141) but also elevates the chance of developing clinically significant distress symptoms. Specifically, the probability of clinical distress is significantly higher (p < 0.001) among those who have experienced stigma (33.66%) than those who have no such experiences (12.62%). Conclusion The public should be aware of the distress‐inducing impact of stigma related to COVID‐19 and prevent it from causing more harm to certain individuals and groups.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".