Asian Healthcare Workers and Their Experiences of Racism in North America: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: The rising rates of anti-Asian sentiments has recently been called into question by several community activists and scholars. While this collective work has heightened awareness to address anti-Asian racism, the experiences of Asian healthcare workers in particular remains limited. PURPOSE: To map the existing literature on anti-Asian racism experienced by Asian healthcare workers in North American healthcare settings, identify gaps in the current literature base, and inform future areas of anti-Asian racism research. METHODS: A scoping review following Arksey and O'Malley's (2005) methodology with updated guidance by Levac et al. (2010) and Peters et al. (2020) was undertaken. FINDINGS: A total of 3565 articles from database searches were identified from eight databases, with 64 full text articles screened and 15 articles included in this review. Anti-Asian racism amongst healthcare workers has been conceptualized, studied, and understood in three broad categories: levels of racism, descriptions of anti-Asian racism, and the impact of racism. In 60% of the included articles, interpersonal level of racism was solely studied, while 40% articles simultaneously studied interpersonal and institutional levels of racism. Anti-Asian racism was described through three key perspectives: otherness, inferior professional status, and general racial discrimination. Lastly, the impact of Asian healthcare workers' experiences of anti-Asian racism was studied by exploring the impact on mental health and barriers to career advancement. CONCLUSION: Despite the presence of anti-Asian racism, the limited literature examining the complexities of the experiences of anti-Asian racism for Asian healthcare workers is concerning. Future scholarship requires further investigation that comprehensively explores the multiple pathways of anti-Asian racism, the contestation of monolithic stereotypes, and how Asian healthcare workers negotiate both hypervisibility and invisibility within healthcare spaces.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 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".