Stigma, discrimination, and attitude towards the Chinese community in the USA and Canada during the outbreak of COVID-19
Bibliographic record
Abstract
Introduction: The COVID-19 outbreak, declared a global pandemic by the WHO, raises some serious health, as well as discrimination concerns worldwide. This exploratory study outlines the knowledge, stigma, and discrimination towards the Chinese community in the USA and Canada at the onset of the pandemic. Methods: An online community-based, opt-in descriptive survey was conducted from the 20th of February 2020 through the 13th of March 2020. The study collected data with anonymity about demographics, travel history, COVID-19 knowledge, awareness, as well as stigmatization and discrimination against the Chinese community. Data was compiled with excel using descriptive statistics and Chi-square for the analysis. Results: In this study, 148/172 (86%) respondents (P<0.05) knew how COVID-19 can spread from one person to another and 123/175 (71.5%) knew how to avoid getting the infection(P<0.05). There was some reported stigma against the Chinese community, particularly during the early days of the outbreak when it was still contained within the Chinese borders; 11/172 (6.4%) participants (P<0.05) indicated that only Chinese infected COVID-19 individuals need to be quarantined with 23/172 (13.4%) avoiding only the Chinese community(P<0.05); which demonstrates the lack of information and protocol available to the public at the time, as well as a general lack of understanding of COVID-19 by the general public. Furthermore, 52/172 (30%) of the respondents (P<0.05) blamed people from China for the COVID-19 outbreak; while 23/172 (13%) people (P<0.05) said they would avoid Chinese people and/or their communities. The level of knowledge, stigma, and discrimination with the respondent’s socio-demographic characteristics was compared as well. Conclusion: As this was a newly diagnosed disease, lack of knowledge caused anxiety and fear among some people, which thus played the main role in the rising cases of Chinese community stigma and discrimination reported.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".