Health, health care, and economic experiences of immigrants in Canada: an interdisciplinary analysis
Bibliographic record
Abstract
Understanding immigrants’ use and preferences for health care services is essential to be able to shape a health care system that is equitable. Without doing so, we risk population health deterioration that can be both costly for immigrants and society. Given the bidirectional relationship between income and health, it also becomes important to understand how immigrants respond economically to health shocks. With this understanding, the objectives of this thesis are as follows: 1) investigate whether neighbourhood immigrant concentration influences the relationship between immigrant status and mental health problems and mental health service use among youth and children in Ontario, 2) assess the impacts of a cerebrovascular or cardiovascular health shock on income variables (i.e., employment income, governmental income, total income, and household income) in heterogenous immigrant populations in Canada, and 3) explore preferences for home care and long-term care among older adults in Ontario. Novel findings from each of the studies include: 1) immigrants show lower odds of having elevated mental health problems and using mental health services compared to non-immigrants. The probability of using mental health services among immigrants is lower in immigrant-concentrated neighbourhoods, 2) a cardiovascular or cerebrovascular health shock does not cause a significant reduction in employment income. Also, the impact of a cardiovascular or cerebrovascular health shock on income variables is not significantly different than the impact of other types of health shocks, and 3) individuals are willing to pay approximately $4000 extra per month for home care compared to long-term care. Other attributes preferred include having a private room, living at a short distance to family/friends, and having culturally adapted care. This program of research advances intersectoral research on immigrant health and health care use.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".