Supporting Female Survivors of Gender-Based Violence Experiencing Homelessness: Outcomes of a Health Promotion Psychoeducation Group Intervention
Bibliographic record
Abstract
Homelessness is an important risk factor for gender-based violence (GBV), particularly among youth, and disproportionally affects women and girls. Survivors of GBV experience enduring and severe physical, psychological, and sexual health problems. Although key elements in service delivery for survivors of GBV have been identified, little is known about outcomes of community-based programs aiming to assist homeless and unstably housed youth experiencing GBV. This longitudinal study aimed to quantitatively evaluate changes in mental health and well-being outcomes in female identified youth experiencing GBV and homelessness, 12 months after enrolment in a community-based, trauma-informed, brief group psychoeducation intervention. Standardized survey measures were administered at baseline, 6 and 12 months for 70 participants, recruited between February 2017 and April 2019, assessing quality of life, psychological distress, traumatic symptoms, substance use, resilience, victimization, and sense of mastery. Linear mixed models were used to examine longitudinal changes in quality of life as well as secondary outcomes among study participants. After 12 months, quality of life increased significantly among participants (p = 0.009), and the 12-month victimization score was significantly decreased relative to baseline (p = 0.05). Changes in other outcomes were not statistically significant. Findings suggest that community-based brief group psychoeducation interventions may be a promising approach to improving outcomes for this disadvantaged population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".