The effect of forensic events on health status and housing stability among homeless and vulnerably housed individuals: A cohort study
Bibliographic record
Abstract
We sought to characterize the association between a forensic event (arrest or incarceration) with housing vulnerability and mental and physical health status over a four-year follow-up among a cohort of homeless and vulnerably housed individuals in Vancouver, Toronto and Ottawa. Data were obtained from the Health and Housing in Transition Study, a prospective cohort study of homeless and vulnerably housed individuals between 2009 and 2012. Participants were interviewed in-person at baseline (N = 1190) and at four annual follow-up time points. We used generalized estimating equations to characterize the independent associations between a forensic event and the number of residential moves and SF-12 physical and mental health component scores over the four-year follow-up period. We analyzed data from 1173 homeless and vulnerably housed participants. Forensic events were reported by 446 participants at baseline. In multivariate analyses, a history of forensic event in the preceding twelve months was independently associated with an increased number of residential moves over the four-year follow-up period (ARR 1.24; 95% CI 1.19-1.3). It was not, however, independently associated with a change in physical or mental health status (respective ß-estimates; 95% CI: -0.34; -1.02, 0.34, and -0.69; -1.5, 0.2). Female gender and a history of problematic substance use were significantly associated with all three primary outcomes. This suggests arrest or incarceration is associated with increased housing vulnerability. The results underline the importance of supporting individuals experiencing arrest or incarceration with post-release planning in order to obtain stable housing after discharge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".