Experiences of racism and race-based traumatic stress symptoms among people of Chinese heritage in Canada: The moderating role of resilience.
Bibliographic record
Abstract
OBJECTIVE: Experiences of racism have been linked to increased race-based traumatic stress symptoms. However, studies in this area have not focused solely on Chinese individuals in North America, such as those in the United States and Canada. Furthermore, little is known about protective factors that may buffer the adverse impact of racism on race-based traumatic stress symptoms. Addressing these limitations, this study examined (a) the relationship between racism and race-based traumatic stress symptoms and (b) the protective role of both individual and collective resilience in the association between racism and race-based traumatic stress symptoms among people of Chinese heritage in Canada. METHOD: = 33.9; 46.59% women) completed self-report questionnaires. RESULTS: Moderation analyses using SPSS PROCESS Model 1 showed that greater experiences of racism were significantly associated with increased race-based traumatic stress symptoms. Collective resilience, but not individual resilience, buffered the adverse effects of racism on race-based traumatic stress symptoms. CONCLUSION: This study enhances the understanding of race-based traumatic stress among Chinese individuals and highlights the need to foster collective resilience and community-based support systems for this population. (PsycInfo Database Record (c) 2026 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".