COVID-19–Related Racism and Mental Health Among Asian Americans: Integrative Review
Bibliographic record
Abstract
Background: Racism against Asian Americans escalated during the COVID-19 pandemic. About 31%-91% of Asian American adults and children reported experiencing various types of racism during the pandemic. According to the Federal Bureau of Investigation hate crime statistics, anti-Asian hate crime incidents increased from 158 in 2019 to 279 in 2020 and 746 in 2021. In 2022, the incidents decreased to 499, corresponding to the downward trend of the pandemic. The degree of impact racism has on mental health and wellness among Asian Americans requires investigation, specifically during the COVID-19 pandemic. Objective: We aim to describe racism-related mental health problems experienced by Asian Americans living in the United States and propose implementation strategies for mitigating their consequences. Methods: We conducted an integrative review of peer-reviewed publications in English reporting anti-Asian sentiments and racism's impacts on mental health among Asian Americans in the United States. Results: The 29 eligible articles report on studies that utilized cross-sectional survey designs with various sample sizes. Racism is directly correlated with the prevalence of depression and anxiety experienced by victims of racist acts. The prevalence of in-person direct racism (racist expression aimed directly at the victim) is lower than in-person indirect racism (racist expression aimed at the ethnic group the victim belongs to). During the COVID-19 pandemic, the incidence of explicit online racism was lower than online indirect racism. Conclusions: COVID-19-related racism exacerbated preexisting racism, contributing to worse depression and anxiety among Asian Americans. To address this issue, we propose 2 main approaches: increase public awareness and education about recognizable racist sentiments/acts and systematized reporting of racially motivated crimes to guide political action. At an individual level, culturally responsive, trauma-informed interventions promoting cultural support and cohesion for various Asian American groups will foster this empowerment. These proposed actions will help alleviate racism by reducing stereotypes, empowering victims, and chipping away at the systemic racism structure.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".