The deterioration of health status among immigrants to Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A growing body of literature suggests that immigrants to Canada experience deterioration in their health status after settling in the country. While self-selection processes and Canadian immigration policy ensure that, at the time of arrival, immigrants are healthier than the Canadian-born population, this health advantage does not persist over time. This study uses new data from the Longitudinal Survey of Immigrants to Canada (N=7720) to examine how health transitions vary among immigrants. Logistic regression analyses indicate that visible minorities and immigrants who experienced discrimination or unfair treatment are most likely to experience a decline in self-reported health status. The results also confirm a clear inverse socioeconomic gradient with respect to increasing levels of feelings of sadness, depression and loneliness. These findings reflect important dimensions driving population health patterns in Canada, a country with a highly lauded health care system based on the principles of universality and comprehensiveness. Our findings suggest that discrimination and inequality partly drive the health transitions of immigrants. These factors, which largely operate outside of the formal health care system, need to be understood and addressed if health inequities are to be reduced.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it