Injury among the immigrant population in Canada: exploring the research landscape through a systematic scoping review
Bibliographic record
Abstract
BACKGROUND: Injuries are the leading cause of death among younger Canadians and represent a large economic burden on the Canadian population. Although immigrants comprise more than 20% of the Canadian population, the research landscape on injury in this group is unclear. We conducted a scoping review to summarize existing research regarding injuries among Canadian immigrants to identify research gaps and future research opportunities. METHODS: Relevant electronic databases of peer-reviewed articles and grey literature were systematically searched. Original articles were selected based on predefined criteria. Relevant information from the articles was extracted and reported in the review. RESULTS: After a comprehensive search, screening and full-text evaluation, 28 articles were selected for the synthesis. Of the injuries that have been studied among Canadian immigrants, the majority focused on occupational injuries, followed by road traffic accidents. Of the 28 studies, 16 were quantitative and 12 were qualitative. The research themes among occupational injury papers centred on factors leading to injury, factors leading to delayed reporting and compensation of injury and post-occupational injury experiences. Language barriers, informal training and the mismatch between education and occupation among immigrants were found to be the most frequent determinants of injury risk. CONCLUSIONS: The synthesized knowledge in this scoping review offers an understanding of the current research landscape on injury among immigrants that can be used to assist policymakers, service providers, employers and researchers regarding injuries in this population.
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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 | 0.026 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.031 | 0.042 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".