A Rapid Assessment of the Impact of COVID-19 on Asian Americans: Cross-sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: The diverse Asian American population has been impacted by the COVID-19 pandemic, but due to limited data and other factors, disparities experienced by this population are hidden. OBJECTIVE: This study aims to describe the Asian American community's experiences during the COVID-19 pandemic, focusing on the Greater San Francisco Bay Area, California, and to better inform a Federally Qualified Health Center's (FQHC) health care services and response to challenges faced by the community. METHODS: We conducted a cross-sectional survey between May 20 and June 23, 2020, using a multipronged recruitment approach, including word-of-mouth, FQHC patient appointments, and social media posts. The survey was self-administered online or administered over the phone by FQHC staff in English, Cantonese, Mandarin, and Vietnamese. Survey question topics included COVID-19 testing and preventative behaviors, economic impacts of COVID-19, experience with perceived mistreatment due to their race/ethnicity, and mental health challenges. RESULTS: Among 1297 Asian American respondents, only 3.1% (39/1273) had previously been tested for COVID-19, and 46.6% (392/841) stated that they could not find a place to get tested. In addition, about two-thirds of respondents (477/707) reported feeling stressed, and 22.6% (160/707) reported feeling depressed. Furthermore, 5.6% (72/1275) of respondents reported being treated unfairly because of their race/ethnicity. Among respondents who experienced economic impacts from COVID-19, 32.2% (246/763) had lost their regular jobs and 22.5% (172/763) had reduced hours or reduced income. Additionally, 70.1% (890/1269) of respondents shared that they avoid leaving their home to go to public places (eg, grocery stores, church, and school). CONCLUSIONS: We found that Asian Americans had lower levels of COVID-19 testing and limited access to testing, a high prevalence of mental health issues and economic impacts, and a high prevalence of risk-avoidant behaviors (eg, not leaving the house) in the early months of the COVID-19 pandemic. These findings provide preliminary insights into the impact of the COVID-19 pandemic on Asian American communities served by an FQHC and underscore the longstanding need for culturally and linguistically appropriate approaches to providing mental health, outreach, and education services. These findings led to the establishment of the first Asian multilingual and multicultural COVID-19 testing sites in the local area where the study was conducted, and laid the groundwork for subsequent COVID-19 programs, specifically contact tracing and vaccination programs.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".