Recognizing and responding to women experiencing homelessness with gendered and trauma-informed care
Bibliographic record
Abstract
BACKGROUND: The purpose of this study is to highlight the experiences of women who are often hidden in what we know and understand about homelessness, and to make policy and practice recommendations for women-centred services including adaptations to current housing interventions. METHODS: Three hundred survey interviews were conducted with people experiencing homelessness in Calgary, Alberta, Canada. The survey instrument measured socio-demographics, adverse childhood experiences, mental and physical health, and perceived accessibility to resources. Eighty-one women participants were identified as a subsample to be examined in greater depth. Descriptive statistics and logistic regressions were calculated to provide insight into women respondents' characteristics and experiences of homelessness and how they differed from men's experiences. RESULTS: Women's experiences of homelessness are different from their male counterparts. Women have greater mental health concerns, higher rates of diagnosed mental health issues, suicidal thoughts and attempts, and adverse childhood trauma. The results should not be considered in isolation, as the literature suggests, because they are highly interconnected. CONCLUSION: In order to ensure that women who are less visible in their experiences of homelessness are able to access appropriate services, it is important that service provision is both gender specific and trauma-informed. Current Housing First interventions should be adapted to ensure women's safety is protected and their unique needs are addressed.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".