The Psychological Effects and Experiences of the Asian Canadian Population During the COVID-19 Pandemic
Bibliographic record
Abstract
This narrative qualitative research study explored experiences of anti-Asian racism from the perspective of four Asian Canadians in the Greater Toronto Area (GTA) during the COVID-19 pandemic. The research aims to illustrate the impact of anti-Asian racism following the emergence of the COVID-19 virus and how it affected the mental health and well-being of these individuals. Through the theoretical framework of Critical Race Theory, this research focused on presenting the stories from the perspective of people of colour and examines how practices of racial discrimination have suppressed Asian Canadians. This research paper utilizes a thematic narrative analysis to dissect the findings of the study. The themes that emerged from the study included the circulation of misinformation through news and social media, experiences of micro-aggressions, the development of stress and anxiety, and calls for preventative measures. The findings provided insight into how some forms of racism may be experienced in Canada during the COVID-19 pandemic and its mental health implications. Following the analysis of this study, implications for future social work practice and research are discussed. These implications include the advocacy for further longitudinal research and push for policy change to protect and support Asian populations in Canada.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.033 | 0.018 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".