The Association of Immigration and Resettlement on Mental Health and Disability Outcomes: Evidence from Administrative Data
Bibliographic record
Abstract
Canada’s immigration system is recognized for its diversity, offering various pathways for individuals worldwide to resettle. Immigrants arrive through distinct streams, each shaped by specific pre-arrival criteria and supported by post-arrival services. Initially, immigrants tend to exhibit better health than the Canadian-born population, a concept known as the "healthy immigrant effect." However, this health advantage diminishes over time, impacting not only physical but also mental health, with the latter receiving less research attention. Despite the growing recognition of mental health as a crucial component of well-being, mental health outcomes among immigrant groups have been less frequently studied than physical health outcomes. Mental health trajectories differ considerably across immigrant categories, and the effect of pre-arrival criteria and post-immigration support on mental health outcomes and immigrant resettlement to Canada remains underexplored. This dissertation addresses this gap by utilizing nineteen years of health administrative data to examine mental health outcomes among different immigrant groups in Ontario, Canada. It comprises three interrelated studies that explore how immigration category, resettlement patterns, and post-mental health diagnosis outcomes shape immigrants' mental health and well-being. The first study identifies differences in mental health outcomes based on immigration category, age at immigration, time period, and immigration cohort effects, illustrating the complexity of mental health trajectories. The second study examines the role of resettlement experiences, particularly residential mobility, and the impact on mental health outcomes among immigrant groups. The third study investigates the link between the first visit to acute care with a mental health diagnosis and subsequent receiving formal disability support, revealing how these outcomes differ by immigration category and their implications for long-term well-being. Collectively, these studies highlight the need for targeted mental health interventions that account for the unique experiences of each immigration category to promote well-being and support successful acclimatization to Canada. This research also underscores the importance of further exploration into the mechanisms driving these differences, providing policymakers with crucial insights into the intersection of mental health, immigration policy, and resettlement. Ultimately, this dissertation contributes to the field of immigrant mental health research in Canada, offering key findings for future policy development.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".