Lived experiences of Asian Canadians encountering discrimination during the COVID-19 pandemic: a qualitative interview study
Bibliographic record
Abstract
BACKGROUND: Asian Canadians have experienced increased cases of racialized discrimination after the first emergence of SARS-CoV-2 in China. This study examined how the COVID-19 pandemic has affected Asian Canadians' sense of safety and belonging in their Canadian (i.e., geographical) communities. METHODS: We applied a qualitative description study design in which semistructured interviews were conducted from Mar. 23 to May 27, 2021. Purposive and snowball sampling methods were used to recruit Asian Canadians diverse in region, gender and age. Interviews were conducted through Zoom videoconference or telephone, and independent qualitative thematic analysis in duplicate was used to derive primary themes and subthemes. RESULTS: Thirty-two Asian Canadians (median age 35 [interquartile range 24-46] yr, 56% female, 44% East Asian) participated in the study. We identified 5 predominant themes associated with how the COVID-19 pandemic affected the participants' sense of security and belonging to their communities: relation between socioeconomic status (SES) and exposure to discrimination (i.e., how SES insulates or exposes individuals to increased discrimination); politics, media and the COVID-19 pandemic (i.e., the key role that politicians and media played in enabling spread of discrimination against and fear of Asian people); effect of discrimination on mental and social health (i.e., people's ability to interact and form meaningful relationships with others); coping with the impact of discrimination (i.e., the way people appraise and move forward in identity-threatening situations); and implications for sense of safety and sense of belonging (i.e., people feeling unable to safely use public spaces in person, including the need to remain alert in anticipation of harm, leading to distress and exhaustion). INTERPRETATION: During the COVID-19 pandemic, Asian Canadians in our study felt unsafe owing to the uncertain, unexpected and unpredictable nature of discrimination, but also felt a strong sense of belonging to Canadian society and felt well connected to their Asian Canadian communities. Future work should seek to explore the influence of social media on treatment of and attitudes toward Asian Canadians.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.027 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".