Identifying Gaps in Health Research among Refugees Resettled in Canada
Bibliographic record
Abstract
Abstract While the global number of resettled refugees rises annually, the summaries of research on refugee health needs in countries of asylum remain sparse. We conducted a systematic review of published research on refugee health in Canada in order to: (i) identify studies addressing health outcomes among refugees recently resettled in Canada; (ii) identify general trends in health research conducted in Canada among refugee populations; (iii) identify significant gaps in current knowledge of health‐related issues among refugees recently resettled in Canada; (iv) evaluate the quality and consistency of available information; (v) develop a summary of available research results; and (vi) identify priorities for future research. A search of several major citation indices resulted in the analysis of 196 research reports after reviewing more than 5,000 articles. This review is timely, systematic and inclusive; furthermore, potential biases in methodology are clearly assessed. The results indicate an immediate need to address specific gaps in health knowledge for refugee populations and lead us to draw five primary conclusions. First, mental health outcomes dominate the research landscape. Second, cross‐sectional studies are most commonly the study design of choice. Third, studies examining some aspect of health among refugees from Asia dominate the literature. Fourth, there is a notable lack of information on cardiovascular diseases and its antecedents. Fifth, indications show that screenings for pre‐existing conditions are biased towards communicable diseases. These findings have implications for health monitoring, evaluation and policy affecting the health of refugees resettled in Canada and elsewhere.
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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 | 0.044 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.027 | 0.039 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".