Physical Activity Buffers the Adverse Impacts of Racial Discrimination on Allostatic Load Among Indigenous Adults
Bibliographic record
Abstract
BACKGROUND: Racial discrimination has been associated with biological dysfunction among ethnic minorities. The extent to which regular physical activity (PA) may buffer this association is unknown. PURPOSE: To examine the association between past-year racial discrimination and allostatic load (AL) stratified by PA within a sample of Indigenous adults. METHODS: Data were collected from Indigenous adults attending university in a city in western Canada between 2015 and 2017. The Experiences of Discrimination Scale was used to assess discrimination and the Godin-Shephard Leisure-Time Physical Activity Questionnaire assessed PA. A composite of seven biomarkers assessing neuroendocrine, cardiovascular, metabolic, and immune system function measured AL. Linear regression models examined associations adjusted for confounders (N = 150). RESULTS: In the insufficiently active group, every 1 point increase in racial discrimination (up to a maximum of 9) resulted in approximately one third of a point increase in AL score. In the sufficiently active group, the association between racial discrimination and AL score was not statistically significant. CONCLUSIONS: A growing body of research suggests racial discrimination is associated with multisystem biological dysregulation and health risks. Increased action to address racism in society is a priority. As that work unfolds, there is a need to identify effective tools that racialized groups can use to buffer the effects of racism on their health. The present findings suggest that engagement in regular PA may attenuate the pernicious effects of discrimination on biological dysfunction.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".