Gender, Race, and Immigrant Status: Intersecting Implications for Health in Middle and Later Life
Bibliographic record
Abstract
Abstract Although the negative implications of gender, race and immigrant inequalities for health and well-being in the middle and later years of life are well-documented, there is a lack of research addressing their combined implications as well as the mechanisms linking them to various health-related outcomes. Yet, as intersectionality theory reminds us, the consequences of gender, race, immigrant and other inequalities for physical and mental health outcomes must be understood in terms of these overlapping social identities. Moreover, linking intersectionality to stress process theory provides us with an explanation of the mechanisms potentially linking intersecting structural inequalities to health outcomes. This paper draws on data from the Canadian Longitudinal Study on Aging (CLSA - N=51,338) to assess the additive and interactive implications of gender, race and immigrant status for physical and mental health outcomes, together with the mediating effects of primary and secondary stressors on these outcomes. The results of a series of weighted least squares regression analyses suggest that immigrant status interacts with race and/or gender to influence health outcomes. Socioeconomic and other stressors also play a role in linking these intersecting structural inequalities to health outcomes. Overall, our findings provide initial support for the value of linking intersectionality and stress process frameworks for an understanding of the health implications of structural inequalities in middle and later life.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".