All-cause and cause-specific hospitalization rates among temporary and permanent residents living in Canada: A linkage study
Bibliographic record
Abstract
OBJECTIVE: Increased understanding of migrant health outcomes is important for health policy and planning and to support continuity of care for Canadian newcomers. The objective of this study is to expand on previous migrant health research by examining age-standardized hospitalization rates (ASHR) among temporary residents (TRs) and permanent residents (PRs) living in Canada from 2014 to 2018. METHODS: Hospitalization outcomes were obtained by linking administrative health databases to the Longitudinal Immigration Database. TRs and PRs were characterized by covariates including age, sex, migration category, and immigration status transition. All-cause and select cause-specific ASHRs were calculated, including hospitalizations for cancer, injury, and mental and health conditions. RESULTS: All-cause ASHRs were lower among TRs than among PRs, with variations observed within specific migration categories. Among TRs, the ASHR was highest for temporary foreign workers. Workers had the highest ASHR for cancer and injury, while asylum claimants had the highest ASHR for mental health conditions. Among PRs, ASHRs were highest for refugees overall and for all specific causes examined. People who transitioned from TR to PR status had higher ASHRs overall compared to those who did not. CONCLUSION: Observed ASHR differences between TRs and PRs, and among those with immigration status transitions and within specific migration categories, may be related to selection criteria by migrant stream, differential access to healthcare resources, preventive health behaviours, and different exposures influencing health needs. Additional research on characteristics associated with migrant health can inform post-arrival health planning and continuity of care.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".