The influencing factors of discrimination against recovered Coronavirus disease 2019 (COVID-19) patients in China: a national study
Bibliographic record
Abstract
BACKGROUND: Over 26 million recovered COVID-19 patients will suffer from discrimination in work, education and social interactions. We analyzed the determinants of discrimination against recovered COVID-19 patients and suggest policy recommendations to reduce such discrimination. METHODS: Twenty-seven Chinese cities were selected randomly based on their geographical location and GDP rank. One hundred adults were interviewed in each city with an equal number of men and women and three urban residents for every two rural residents. A multiple ordered logistic regression model was used to assess the associations between potential determinants and the COVID-19 discrimination level. RESULTS: Of 2377 participants, 79.76% displayed discrimination toward recovered COVID-19 patients. The female discrimination level was 1.25 times that of males; the discrimination level increased with age; and was occupation-specific, with physicians' (OR = 0.352) and students' (OR = 0.553) discrimination level lower than that of farmers. The discrimination level of participants from the central regions was 1.828 times, and the eastern region 1.504 times, that of participants from western region. The participants' discrimination level was lower when they scored higher in transmission knowledge, prevention knowledge and other COVID-19 knowledge, treatment methods and quarantine time. CONCLUSION: Sex, age, occupation, infections of relatives and friends, regions and scores on COVID-19 knowledge were determinants of discrimination level against recovered COVID-19 patients. In contrast with qualitative studies, our quantitative study recommends targeted education campaigns, focusing on physicians, women, older people and certain occupations. Only the COVID-19 vaccination program for the whole population will resolve the COVID-19 discrimination problem.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".