COVID-19 stigmatization after the development of effective vaccines: Vaccination behavior, attitudes, and news sources
Bibliographic record
Abstract
OBJECTIVE: To compare COVID-19 stigmatization at two pandemic time points (1) August 2020-during lockdowns and prior to vaccine rollout, and (2) May 2021-during vaccine rollout, when approximately half of U.S. adults were vaccinated. METHODS: Comparison of COVID19-related stigmatization and associated factors in two national internet surveys conducted in August 2020 (N = 517) and May 2021 (N = 812). Factors associated with endorsing stigmatization were identified using regression analysis. The main outcomes included endorsement of stigmatization and behavioral restrictions towards persons with COVID-19 and towards persons of Chinese descent. A previously developed "stigmatizing attitudes and behavioral restrictions" scale was adapted to measure the intersection of negative attitudes toward COVID-19 disease and negative attitudes toward persons of Chinese descent. RESULTS: COVID-19 related stigmatization declined significantly from August 2020 to May 2021. Many factors were associated with stigmatizing in both surveys: full time employment, Black race, Hispanic ethnicity, worry about contracting COVID-19, probable depression, and Fox News and social media as sources of information (all positively associated), and self-assessed knowledge about COVID-19, contact with Chinese individuals, and publicly funded news as sources (all negatively associated). Positive attitudes toward vaccination were associated with stigmatization. CONCLUSIONS: COVID-19 related stigmatization reduced substantially over these two points in the pandemic, with many continuities in the factors associated with stigmatizing. Despite the reduction in stigmatizing, however, some stigmatizing attitudes for both COVID-19 and Chinese individuals remained.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".