Racial Discrimination, Cultural Resilience, and Stress
Bibliographic record
Abstract
OBJECTIVE: Racial discrimination is a social determinant of health for First Nations people. Cultural resilience has been regarded as a potentially positive resource for social outcomes. Using a compensatory model of resilience, this study sought to determine if cultural resilience (compensatory factor) neutralized or offset the detrimental effect of racial discrimination (social risk factor) on stress (outcome). METHODS: Data were collected from October 2012 to February 2013 (N = 340) from adult members of the Kettle and Stony Point First Nation community in Ontario, Canada. The outcome was perceived stress; risk factor, racial discrimination; and compensatory factor, cultural resilience. Control variables included individual (education, sociability) and family (marital status, socioeconomic status) resilience resources and demographics (age and gender). The model was tested using sequential regression. RESULTS: The risk factor, racial discrimination, increased stress across steps of the sequential model, while cultural resilience had an opposite modest effect on stress levels. In the final model with all variables, age and gender were significant, with the former having a negative effect on stress and women reporting higher levels of stress than males. Education, marital status, and socioeconomic status (household income) were not significant in the model. The model had R(2) = 0.21 and adjusted R(2) = 0.18 and semipartial correlation (squared) of 0.04 and 0.01 for racial discrimination and cultural resilience, respectively. CONCLUSIONS: In this study, cultural resilience compensated for the detrimental effect of racial discrimination on stress in a modest manner. These findings may support the development of programs and services fostering First Nations culture, pending further study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".