The Experiences of Women Transitioning from Unhoused to Housed in High-Income Countries: A Systematic Review and Meta-Aggregation
Bibliographic record
Abstract
There are pathways in and out of homelessness that are gender-specific. However, little is known about the range of existing studies on the experiences of women who recently transitioned from homeless to housed in high-income countries. To date, no systematic reviews have brought together qualitative analyses from studies exploring women’s experiences who are re-housed in high-income countries. To fill this gap in existing literature, we performed a systematic review, utilizing the methods described by the Joanna Briggs Institute (JBI) (Lockwood et al., 2020) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Moher et al., 2010) to understand the meaning and experience of women exiting homelessness in high-income countries. We combined the search terms related to women, homelessness, and housing transitions and deployed them in six databases. Our research yielded 3025 titles and abstracts following the removal of duplicates. After conducting a title and abstract screening, 91 full-text articles remained. 14 articles were deemed to meet pre-established inclusion criteria. Our meta-aggregation revealed four themes generated through our analysis: a) ongoing presence of trauma in women’s lives; b) healing through providing support to others; c) lack of control over circumstances that enable survival; and d) support in housing decreases marginalization. This review deepens knowledge of the current research priorities as well as practical policy and practice strategies that can be utilized to support recently rehoused women to prevent recurrent periods of homelessness and its devastating impacts.
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 | 0.026 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.021 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".