COVID discrimination experience: Chinese Canadians’ social identities moderate the effect of personal and group discrimination on well-being.
Bibliographic record
Abstract
OBJECTIVE: The coronavirus disease (COVID-19) pandemic has amplified preexisting racism and xenophobia. In this study, we investigated (a) whether perceived personal and group discrimination make distinct contributions to Chinese Canadians' negative affect and concern that the heightened discrimination they experienced during the pandemic will continue after the pandemic; (b) whether Canadian and Chinese identities and social support moderate the effect of discrimination on this concern; and (c) whether race-based rejection sensitivity (RS) explains why each type of discrimination predicts negative affect and expectation of future discrimination. METHOD: = 42.74, 53.3% females) completed a questionnaire assessing personal and group discrimination, Chinese and Canadian identity, a short form of race-based RS, negative affect, and expectation of future discrimination. RESULTS: Personal and group discrimination were intercorrelated and positively associated with negative emotion and expectation of future discrimination. Chinese Canadians who identified more strongly as Chinese experienced a less adverse impact related to group discrimination. However, those who identified more (vs. less) strongly as Canadians were more likely to be impacted by personal discrimination. Finally, path analysis revealed that both personal and group discrimination were positively associated with RS, which in turn predicted an expectation that long-lasting racism would continue after the pandemic. CONCLUSION: Group and personal discrimination play different roles in Chinese Canadians' experiences during and expectations after the pandemic. Maintaining Chinese identity can be beneficial to Chinese Canadians, particularly in mitigating the negative effect of group discrimination during the pandemic. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".