Homelessness and Intimate Partner Violence: Women's Experiences With Accessing Formal Support Services and the Impact of Their Intersecting Identities
Bibliographic record
Abstract
Many women experiencing homelessness and intimate partner violence (IPV) often do not use formal support services due to barriers such as finances, inaccessibility, controlling partners, and stigma. The current literature lacks studies that examine the formal service experiences of women who have undergone both homelessness and IPV. Therefore, the current study used a qualitative-dominant design and explored the formal service needs of these women, barriers and facilitating factors associated with accessing services, and how the women's identities affected their experiences with formal services. Interviews with 10 women were conducted, and a reflexive thematic analysis was conducted using a critical feminist and intersectionality lens. The most common factors that led to homelessness were conflict and precarious circumstances leading to housing instability. Common barriers were systemic, psychological, dismissal, minimization, and financial barriers. The most common needs were health, finances, and basic necessities. The most common facilitating factors were support networks and resilience, growth, and proactivity. The social positionings most commonly affecting their experiences were class and race/ethnicity. These findings highlighted the voices of marginalized women and can be used to implement positive change in formal services that cater to this subgroup of women.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".