Pathways and trajectories linking housing instability and poor health among low-income women experiencing intimate partner violence (IPV): Toward a conceptual framework
Bibliographic record
Abstract
We used grounded theory to understand pathways and trajectories to housing instability (HI) and poor health among low-income women with experiences of intimate partner violence (IPV). We conducted in-depth interviews during 2010-11 with forty-one women (ages 18-45 years) living in Ontario, Canada. All women reported depressive symptoms in combination with other health problems. In addition to the direct pathway of IPV to poor health, thematic analysis revealed an indirect multi-tiered pathway with complex trajectories among IPV, HI, and poor health. These trajectories included material HI (homelessness, high mobility, evictions, problems paying rent, hiding, and landlord discrimination), psychological HI (feeling unsafe, low self-esteem, and poor control), and social trajectories (financial problems, loss of employment, income, or social networks, and leaving school). These trajectories elevated stress and decreased self-care (unhealthy behaviors, substance abuse, and reduced medical compliance) and exacerbated poor health already compromised by IPV. Depending on her specific context, each woman experienced these pathways and trajectories differently. Moreover, the women's experiences differed across three time periods: before, immediately after, and long after leaving an abusive relationship. Finally, we found that for these women, achieving stable housing was crucial for stabilizing their health.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".