An Intersectional Analysis of Victimization of the Homeless, Mental Health, Guardianship and Housing Status
Bibliographic record
Abstract
The present study examines victimization across a localized homeless population. It is widely agreed upon across the extant literature that those experiencing homelessness are victimized at disproportionately higher rates in comparison to the general population. Utilizing routine activities and lifestyles theories, this paper examined how varying degrees of housing and mental health issues affect the likelihood of victimization for those experiencing homelessness. It was hypothesized that lower levels of housing and higher degrees of mental health issues exacerbate the high rates of victimization across the homeless population. Utilizing a routine activities perspective, this study conceptualized housing as a measure of guardianship. This study utilized a secondary data analysis design. Secondary data was accessed from the Winnipeg At Home/Chez Soi project. Using negative binominal regression, it was found that there is a relationship between mental health, guardianship, and victimization. Analyses provided partial support for the hypotheses that greater mental health challenges contribute to a higher propensity of victimization. Although the results illustrated that greater levels of stable housing did have a mitigating effect on victimization at the 12-month time period serious mental health issues were found to be a considerably stronger predictor of victimization than guardianship through housing. The findings suggest that proximity to high crime areas, certain lifestyle factors and individual activities may still account for much of high victimization rates for the homeless, despite an increase in provision of housing. Future qualitative inquiry is recommended to better understand the processes that impact victimization for the homeless with mental health issues.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".