Potentially exploring homelessness among refugees: a systematic review and meta-analysis
Bibliographic record
Abstract
Homelessness among refugee populations has been increasing worldwide in recent years, which provides particular challenges for receiving nations. In a context in which formal systems supporting asylum seekers and refugees face high demand relative to their resource levels, the impact this may have on the homelessness sector to provide support and accommodation for asylum seekers and refugees has received scant attention. This paper presents a systematic review that synthesizes the literature on the determinants of Homelessness among refugees. The study reviewed seven electronic databases from 2002 to December 2019. Most studies exploring Homelessness among refugees were drawn from Canada, Australia, the UK., and the U.S. Overall, refugees constitute a vulnerable population at heightened risk of becoming homeless. The complex interplay between individual and structural pathways into homeless also makes it difficult for refugees to exit Homelessness. Our metadata analysis of the literature further supports that housing, family, health, community, political circumstances, and income disproportionately impact refugees. This paper identifies these factors as the root causes of refugee's Homelessness. Homelessness can also cause some issues with these six factors in general. In fact, there is a bidirectional causation relationship between Homelessness and these factors to some degree. However, the focus of this paper is on how these factors can contribute to refugee homelessness.
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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.017 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".