Housing interventions for women experiencing intimate partner violence: a systematic review
Bibliographic record
Abstract
BACKGROUND: Intimate partner violence (IPV) is the leading cause of women's homelessness. However, what works best to respond to the needs of women experiencing IPV and homelessness remains unclear. We aimed to systematically review the effects of housing interventions on the physical, psychosocial, and economic wellbeing of women experiencing IPV. METHODS: In this systematic review, we searched 15 electronic databases and conducted an extensive grey literature and hand reference search between Jan 29, 2020, and May 31, 2021. We included controlled quantitative studies of housing interventions (from emergency shelter to permanent supportive housing) that were reported in English, without time restrictions, and examined any physical, psychosocial, or economic outcomes among women experiencing IPV. We critically appraised included studies using the Cochrane Effective Practice and Organisation of Care criteria and extracted data using a piloted extraction form. We synthesised our results using harvest plots to summarise whether the weight of the evidence suggested benefits, disadvantages, or null effects; patterns by study quality; and evidence gaps. This study is registered with PROSPERO, CRD42020176705. FINDINGS: We screened 23 902 unique records and identified 34 eligible studies with quantitative data on the outcomes of housing interventions among women experiencing IPV. Most studies evaluated the outcomes of either shelter interventions (18 studies [53%]) or shelter plus some other programming (eight [24%]). The remaining eight studies evaluated longer-term housing solutions, including supportive housing (five studies [15%]), critical time interventions (one [3%]), transitional housing (one [3%]), and stay-at-home models (one [3%]). There was no cumulative evidence of disadvantages following any IPV-housing intervention. Evidence of benefits was strongest for mental health outcomes, intent to leave partner, perceived safety, and housing and partner-related stress. Included studies were at high risk of bias across most domains (eg, confounding). INTERPRETATION: There is promising evidence on the continuum of IPV-housing services for women, especially in terms of proximal outcomes, such as mental health, intent to leave partner, safety, and housing stress. However, more research of higher quality is needed, particularly on long-term housing solutions and from outside of the USA. FUNDING: The Social Sciences and Humanities Research Council (430-2021-01176) and Canadian Institutes of Health Research (HSI-166388).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".