Systemic Factors Explain Differences in Low and High Frequency Shelter Use for Victims of Interpersonal Violence
Bibliographic record
Abstract
Intimate partner violence is detrimental to women and children’s health and social outcomes. In order to identify the complex factors that shape help-seeking behaviour and what places women at highest risk of recurrence of violence and shelter use, it is critical to examine how individual and systemic factors influence shelter use. The Healing Journey Project was a longitudinal study conducted across Alberta, Saskatchewan, and Manitoba to identify the experiences of women who were victims of intimate partner violence. A total of 665 women who had previously experienced IPV were interviewed biannually over a four-year period. Descriptive statistics informed probit regressions that then identified several factors that differentiate single frequency shelter users from high frequency users. The results emphasize the importance of using intersectionality theory to recognize the interplay of multiple factors to showcase the complexity of IPV and how it affects shelter use. The results also emphasize how colonialism’s lasting effects are pervasive, alongside the impacts of poverty, intergenerational abuse and structural barriers to housing and childcare. Implications require changes to policy and government funding to enhance access to gender and culturally safe housing with trauma-informed supports to both intervene and potentially prevent multiple experiences of violence.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".