Perceived stigma of COVID-19 patients in Shanghai, China, in the third year of the pandemic: a cross-sectional social impact survey
Bibliographic record
Abstract
INTRODUCTION: Social stigma associated with Covid-19 infection has been reported around the world. This paper investigates the level of self-reported perceived stigma among people infected with COVID-19 in Shanghai, China, in the third year of the pandemic to determine changes in perceived stigma and individual level variables associated with perceived stigma. METHODS: We conducted a self-reported two-part online survey (n = 144 responses) by employing a convenience sampling method of COVID-19 patients in Shanghai. The first part of the survey collects sociodemographic information of the respondents and the second part outlines 24 items of the Social Impact Scale (SIS), which measures individual level factors associated with stigma, namely social rejection, financial insecurity, internalized shame, and social isolation. We ran Wilcoxon signed-ranks test, Kruskal-Wallis test, and linear regression analysis to assess the levels of perceived stigma differences. RESULTS: The study finds that the overall level of self-reported stigma during the COVID-19 lockdowns in Shanghai in 2022 was at a lower level than that compared to the self-reported perceived stigma study in Wuhan in 2020. In Shanghai, the severity of the disease and hospitalization length had most impact on financial insecurity and feelings of social isolation. These experiences were not gendered. Recovery measures, including economic considerations, need to pay particular attention to those who experienced severe disease.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".