The effect of acculturation on the health of new immigrants to Canada between 2001 and 2005
Bibliographic record
Abstract
Poster Presentation When comparing the health of immigrants to the native-born, studies have found what is called a “healthy migrant effect” where immigrants are likely to have a health advantage compared to native-born individuals. In Canada, effect could partially be explained by the strict immigration criteria that select immigrants on their health status (Akresh and Frank, 2008). However, immigrants lose this advantage over time so that their level of health often deteriorates below the one of natives. This deterioration is an important issue for the health of populations in Canada and a challenge to adapt the health system to the needs of immigrants. The Longitudinal Survey of Immigration to Canada (LSIC) provides an original way to assess the effects of acculturation, a process of adopting new cultural norms and practices, which has been often cited as one of the leading causes of immigrant’s health deterioration. The LSIC contains a cohort of 7716 landed immigrants in Canada between October 1st 2000 and September 30th 2001. The objective of this paper is to analyze the effects of acculturation on immigrants’ general health and self-perceived mental health. The analysis is based on multivariate logistic regressions that control for pre-migration and post-migration factors which may potentially confound the relationship between acculturation and health. Our results show that acculturation outcomes proposed by Berry (1997) - integration, assimilation, separation, marginalization- influence the health of immigrants through socioeconomic variables such as education and financial status.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".