Examining Structural and Social Supports Offered to Resettled Refugees in 10 Host Countries – A Scoping Review Acknowledging Health in All Policies
Bibliographic record
Abstract
Abstract Purpose of Review Acknowledging health in all policies, this scoping review aims to describe and compare i) structural and social supports offered by countries participating in the United Nations refugee agency resettlement program and ii) refugees’ and service providers’ experiences with these supports. Recent Findings Structural supports in the 10 countries resettling the largest number of refugees in 2021 (United States [US], Canada, and 8 European nations) were summarized, using official national documentation. A scoping review of published literature (1995–2022) sourced from four databases was conducted to capture met and unmet needs of refugees and service providers related to these supports during the first year of resettlement. Study characteristics were enumerated, and needs were descriptively summarized. We found important differences in structural supports offered to resettled refugees by host countries and regions, particularly with access to healthcare, language training, employment and financial support. The 63 included studies originated from the US (34), Canada, (25) and the United Kingdom (UK) (4), with uneven sub-national distributions. Most studies focusing on healthcare reported unmet needs, with language barriers, lack of culturally sensitive care and logistical challenges described in all three countries. Insufficient language training and unmet economic needs were also often reported. Summary More research on resettled refugees' and service providers’ experiences with structural supports is needed, particularly in Europe and underrepresented regions in the US and Canada. A “Health in All Policies” approach to policies and programs related to resettlement should address unmet needs in healthcare, language training, employment and financial support.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".