All-cause and circulatory disease-related hospitalization, by generation status: Evidence from linked data.
Bibliographic record
Abstract
BACKGROUND: Immigrants tend to have better health than the Canadian-born. However, the "healthy immigrant" effect diminishes over time and varies by source country. This study examines whether lower hospitalization rates persist from the first (G1) to the second generation (G2) of immigrants, compared with other Canadians (G3+). All-cause and circulatory disease-related hospitalization rates were examined by generation, with special attention to people of Chinese and South Asian descent. DATA AND METHODS: Data from the 2006 Census-hospitalization linkage database (which excludes Quebec) were analysed using logistic regression. Age-standardized all-cause (excluding pregnancy) and circulatory disease-related hospitalization rates were derived for the urban population aged 30 or older for the 2006/2007 to 2008/2009 fiscal years. RESULTS: Over the generations, immigrants' all-cause and circulatory disease-related hospitalization rates converged with those of the Canadian population overall. Compared with G3+, the age-adjusted odds of all-cause hospitalization among men were 0.49 (CI = 0.48-0.51) for recent G1 immigrants, 0.78 (CI = 0.77-0.79) for long-term G1 immigrants, and 0.95 (CI = 0.94-0.97) for G2. Adjustments for socioeconomic status reduced the difference, especially between G2 and G3+. For South Asians, rates converged for circulatory disease, notably among men. Hospitalization rates for people of Chinese descent rose across generations, but remained significantly below rates for G3+. INTERPRETATION: The lower circulatory disease-related hospitalization risk experienced by G1 is maintained in G2 among people of Chinese descent, but not among South Asians.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".