Bibliographic record
Abstract
Canada admits between more than 200,000 immigrants every year. National policy emphasizes rigorous selection to ensure that Canada admits healthy immigrants. However, remarkably little policy is directed to ensuring that they stay healthy. This neglect is wrong-headed: keeping new settlers healthy is just, humane, and consistent with national self-interest. By identifying personal vulnerabilities, salient resettlement stressors that act alone or interact with predisposition in order to create health risk, and the personal and social resources that reduce risk and promote well-being, health research can enlighten policy and practice. However, the paradigms that have dominated immigrant health research over the past 100 years--the "sick" and "healthy immigrant," respectively--have been inadequate. Part of the problem is that socio-political controversy has influenced the questions asked about immigrant health, and the manner of their investigation. Beginning with a review of studies that point out the shortcomings of the sick immigrant and healthy immigrant paradigms, this article argues that an interaction model that takes into account both predisposition and socio-environmental factors, provides the best explanatory framework for extant findings, and the best guide for future research. Finally, the article argues that forging stronger links between research, policy and the delivery of services will not only help make resettlement a more humane process, it will help ensure that Canada benefits from the human capital that its newest settlers bring with them.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".