“If I Stay Quiet, the Only Person That Gets Hurt Is Me”: Anti-Asian Racism and the Mental Health of Chinese-Canadian Youth During the COVID-19 Pandemic
Bibliographic record
Abstract
Background and Purpose Despite documented accounts of racial discrimination against Chinese communities during the COVID-19 pandemic, few studies have examined experiences of racism among Canadian youth. This qualitative study explored the experiences of Chinese-Canadian youth during the COVID-19 pandemic and their mental health. Methods A qualitative descriptive research design, informed by Critical Race Theory (CRT), was used for this study. Data was collected using focus groups and image-based elicitation methods. Youth who self-identified as Chinese-Canadian, aged 18–24, and who experienced some account of self-defined racism were included. We analyzed the data using a coding system developed for this study and formulated key themes. Results Our analysis identified three themes: (I) Becoming racialized ; (II) Learning the rules of racism ; and (III) Effects of racism on mental health . We discuss findings in relation to the model minority stereotype, intersectionality of race and gender, and factors leading to a lack of support. Conclusions This study provides evidence that racism had immediate and prolonged effects on the mental health of Chinese-Canadian youth and their relationships with peers, family, and even strangers. Our research suggests the need for enhanced services for Chinese-Canadian youth and other groups experiencing racism.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".