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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".