How Are Non-Medical Settlement Service Organizations Supporting Access to Healthcare and Mental Health Services for Immigrants: A Scoping Review
Bibliographic record
Abstract
Following resettlement in high-income countries, many immigrants and refugees experience barriers to accessing primary healthcare. Local non-medical settlement organizations, such as the Local Immigration Partnerships in Canada, that support immigrant integration, may also support access to mental health and healthcare services for immigrant populations. This scoping review aims to identify and map the types and characteristics of approaches and interventions that immigrant settlement organizations undertake to support access to primary healthcare for clients. We systematically searched MEDLINE, Social Services Abstracts, CINAHL, and PsycInfo databases from 1 May 2013 to 31 May 2021 and mapped research findings using the Social-Ecological Model. The search identified 3299 citations; 10 studies met all inclusion criteria. Results suggest these organizations support access to primary healthcare services, often at the individual, relationship and community level, by collaborating with health sector partners in the community, connecting clients to health services and service providers, advocating for immigrant health, providing educational programming, and initiating community development/mobilization and advocacy activities. Further research is needed to better understand the impact of local non-medical immigrant settlement organizations involved in health care planning and service delivery on reducing barriers to access in order for primary care services to reach marginalized, high-need immigrant populations.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".