Discrimination experienced by Asian Canadian and Asian American health care workers during the COVID-19 pandemic: a qualitative study
Bibliographic record
Abstract
Background: Asian Canadians and Asian Americans face COVID-19–related discrimination. The objective of this qualitative study was to explore the experiences of Asian health care workers dealing with discrimination, with a focus on racial micro-agressions, in Canada and the United States during the COVID-19 pandemic. Methods: We adopted a qualitative descriptive approach. We used convenience and snowball sampling strategies to recruit participants. We conducted individual, in-depth semistructured interviews with Asian health care workers in Canada and the US via videoconferencing between May and September 2020. Eligible participants had to self-identify as Asian and be currently employed as a health care worker with at least 1 year of full-time employment. We used an inductive thematic approach to analyze the data. Results: Thirty participants were recruited. Fifteen (50%) were Canadians and 15 (50%) were Americans; there were 18 women (60%), 11 men (37%) and 1 nonbinary person. Most of the participants were aged 25–29 years (n = 16, 53%). More than half were nurses (n = 16, 53%); the other participants were attending physicians (n = 5), physiotherapists (n = 3), resident physicians (n = 2), a midwife, a paramedic, a pharmacist and a physician assistant. Two themes emerged from the data: a surge of racial microaggressions related to COVID-19 and a lack of institutional and public acknowledgement. Participants noted that they have experienced an increase in racial microaggressions during the COVID-19 pandemic. They have also experienced threats of violence and actual violence. The largely silent organizational response to the challenges being faced by people of Asian descent and the use of disparaging terms such as “China virus” in the early stages of the pandemic were a substantial source of frustration. Interpretation: Asian health care workers have experienced challenges in dealing with racial microaggressions related to COVID-19 in the US and Canada. More research should be done on the experiences of Asian Americans and Asian Canadians, both during and after the pandemic, and supportive measures should be put in place to protect Asian health care workers.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.032 | 0.016 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".