Injury among the immigrant population in Canada: exploring the research landscape through a systematic scoping review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it