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Record W4411346941 · doi:10.1037/tra0001966

Experiences of racism and race-based traumatic stress symptoms among people of Chinese heritage in Canada: The moderating role of resilience.

2025· article· en· W4411346941 on OpenAlexaffabout
Ling Jin

Bibliographic record

VenuePsychological Trauma Theory Research Practice and Policy · 2025
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsRacismRace (biology)PsychologyPsychological resilienceResilience (materials science)PsycINFOStress (linguistics)Social psychologyClinical psychologyGender studiesSociologyPolitical scienceMEDLINE

Abstract

fetched live from OpenAlex

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).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.276
Threshold uncertainty score0.918

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.468
Teacher spread0.422 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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