Can cultural identity clarity protect the well-being of Latino/a Canadians from the negative impact of race-based rejection sensitivity?
Bibliographic record
Abstract
OBJECTIVES: The aim of the present study was to examine the understudied immigration and acculturation experience of the growing Latino/a community in Canada. Specifically, we explored the impact of race-based rejection sensitivity on well-being, and whether cultural identity clarity could help curtail any negative effects. Hypothesis 1 was that race-based rejection sensitivity would be negatively associated with well-being. Hypothesis 2 was that cultural identity clarity would moderate the association between race-based rejection sensitivity and well-being such that Latino/a immigrants lower in cultural identity clarity would experience poorer well-being than those higher in cultural identity clarity. METHOD: A community sample of Latino/a immigrants (N = 136; Mage = 38.21; 51.47% female) completed a survey including measures of race-based rejection sensitivity, cultural identity clarity, bicultural stress, self-esteem, and life satisfaction. RESULTS: Correlation and regression analyses revealed that race-based rejection sensitivity was negatively associated with well-being. Additionally, high cultural identity clarity attenuated the negative association between race-based rejection sensitivity and well-being. CONCLUSION: Results of the present study suggest maintaining clarity over their heritage cultures postimmigration can be beneficial to Latino/a immigrants in Canada, in particular when they are sensitive to cues of racial discrimination. (PsycInfo Database Record (c) 2020 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 | 0.003 |
| 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 | 0.002 | 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".