Understanding the health and housing experiences of refugees and other migrant populations experiencing homelessness or vulnerable housing: a systematic review using GRADE-CERQual
Bibliographic record
Abstract
BACKGROUND: A growing number of migrants experience precarious housing situations worldwide, but little is known about their health and housing experiences. The objective of this study was to understand the enablers and barriers of accessing fundamental health and social services for migrants in precarious housing situations. METHODS: We conducted a systematic review of qualitative studies. We searched the databases of MEDLINE, PsycINFO, CINAHL, Scopus, Web of Science, Social Sciences, Canadian Business & Current Affairs and Sociological Abstracts for articles published between Jan. 1, 2007, and Feb. 9, 2020. We selected studies and extracted data in duplicate, and used a framework synthesis approach, the Bierman model for migration, to guide our analysis of the experiences of migrant populations experiencing homelessness or vulnerable housing in high-income countries. We critically appraised the quality of included studies using the Critical Appraisal Skills Programme checklist and assessed confidence in key findings using the Grading of Recommendations Assessment, Development and Evaluation Confidence in the Evidence from Reviews of Qualitative Research (GRADE-CERQual) approach. RESULTS: We identified 1039 articles, and 18 met our inclusion criteria. The studies focused on migrants from Asia and Africa who resettled in Canada, Australia, the United States, the United Kingdom and other European countries. Poor access to housing services was related to unsafe housing, facing a family separation, insufficient income assistance, immigration status, limited employment opportunities and lack of language skills. Enablers to accessing appropriate housing services included finding an advocate and adopting survival and coping strategies. INTERPRETATION: Migrants experiencing homelessness and vulnerable housing often struggle to access health and social services; migrants may have limited proficiency with the local language, limited access to safe housing and income support, and ongoing family insecurities. Public health leaders could develop outreach programs that address access and discrimination barriers. PROSPERO REGISTRATION: CRD42018071568.
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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 | 0.029 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.026 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".