Race, immigrant status, and inequality in physical activity: An intersectional and life course approach
Bibliographic record
Abstract
Physical activity improves health and well-being, but not everyone can be equally active. Previous research has suggested that racial minorities are less active than their white counterparts and immigrants are less active than their native-born counterparts. In this article, we adopt an intersectional and life course approach to consider how race and immigrant status may intersect to affect physical activity across the life span. This new approach also allows us to test the long-standing habitual versus structural debate in physical activity. Analysing data from two recent cycles of the Canadian Community Health Survey (CCHS, 2015-2016 & 2017-2018), we find that physical activity is only lower among immigrants who are also racial minorities and that the gap is most significant during adulthood, but rather insignificant during adolescence and late life. The findings that inequality in physical activity is more apparent among the most disadvantaged racialised immigrants and among working-age adults when structural influences are greater suggest that inequality in physical activity is rooted in structural inequalities, rather than habitual differences. Finally, we demonstrate that the widely observed 'healthy (racialised) immigrant effect' can be underestimated if inequality in physical activity is not considered.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".