Morbidity among resettled refugees at arrival in Ontario, Canada (1994–2017): a controlled interrupted time series study examining the effect of the Immigration Refugee Protection Act, 2002
Bibliographic record
Abstract
BACKGROUND: Immigration inadmissibility on medical grounds is common among high-income countries. In Canada, the Immigrant and Refugee Protection Act (IRPA) became law in 2002. With humanitarian protection as a priority, IRPA removed medical inadmissibility based on exceeding a cost threshold for the projected use of health and social services for resettled refugees. Our objective was to determine whether resettled refugees arriving in Ontario after IRPA became law (2004-2017) were more likely to exceed the cost threshold than those who arrived before (1994-2002). METHODS: We linked population-based immigration (1994-2017) and healthcare data (1994-2019) in Ontario, Canada and conducted interrupted and controlled interrupted time series (ITS and CITS, respectively) analyses using segmented regression. We examined morbidity prevalence (a proxy for exceeding the cost threshold), in the pre-IRPA and post-IRPA periods among resettled refugees and three control groups-successful asylum seekers, economic immigrants and other Ontario residents. Morbidity prevalence levels and slopes across years were estimated comparing the post-IRPA to pre-IRPA period within resettled refugees and each control group (ITS), and for resettled refugees relative to each control group comparing the same periods (CITS). RESULTS: Morbidity prevalence levels and slopes did not increase significantly within resettled refugees arriving after compared with before IRPA, nor when compared with control groups. Increasing morbidity prevalence among all immigrant groups post-IRPA suggested that subsequent policy changes linked to excessive demand policies may have impacted morbidity. CONCLUSION: Evolving medical inadmissibility policies suggest the need to provide a fulsome evaluation, balancing possible implications with the documented contributions immigrants make to Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".