Perceived and Experienced Anti-Chinese Discrimination and Its Associated Psychological Impacts Among Chinese Canadians During the Wave 2 of the COVID-19 Pandemic
Bibliographic record
Abstract
The current study examined the sociodemographic factors associated with perceived and experienced anti-Chinese discrimination and discrimination as a predictor of psychological distress and loneliness among Chinese Canadians. A cross-sectional online survey was conducted in early 2021 with a sample of 899 Chinese Canadians (i.e., immigrants, citizens, visitors, and international students) during the Wave 2 of the COVID-19 pandemic. Overall, anti-Chinese discrimination was generally associated with younger age and poor financial or health status. Christianity/Catholicism believers were less likely to report perceived discrimination, whereas being married/partnered and living with family reduced the incidences of experienced discrimination. Most importantly, hierarchical linear regression models showed that both perceived and experienced discrimination predicted higher psychological distress (βs = 4.90–7.57, ps ≤ .001) and loneliness (βs = .89–1.73, ps ≤ .003), before and after controlling for all related sociodemographic covariates. Additionally, older age, higher education, better financial or health status could all buffer psychological distress, whereas living with family or in a house and better financial or health status could mitigate feeling of loneliness. The results suggested that discrimination has a robust detrimental impact on mental health conditions among Chinese Canadians.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".