Beyond shelter: a scoping review of evidence on housing in resettlement countries and refugee mental health and wellbeing
Bibliographic record
Abstract
PURPOSE: The number of displaced people globally has rapidly increased in the past decade. Housing is an important social determinant of health and a key contributor to poor health outcomes in refugee and asylum seeker populations. It is important to examine evidence for how housing impacts the mental health of refugees and asylum seekers. This review seeks to analyse the research describing how housing conditions and policies are associated with refugee mental health and wellbeing in high-income resettlement countries (such as the United States, Canada, and Australia). METHODS: A scoping review identified forty-four relevant studies. These studies examined various aspects of housing and/or accommodation and their association with mental health and wellbeing in resettled refugee populations. RESULTS: We found evidence of a relationship between four domains of housing-policy, suitability, environment and time-and mental health. Furthermore, we found evidence that refugees settling in high income countries experienced significant housing issues. Overall, problems with housing quality, location, accessibility (i.e., the nature of systems that govern access to housing) and suitability were associated with poorer mental health outcomes. CONCLUSIONS: In high-income countries, the lack of choice and agency regarding housing contributed to poor mental health outcomes among refugees and asylum seekers. Policies and practices should prioritise the quality, suitability, and accessibility of refugee housing, look at ways to increase choice and agency in resettlement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".