Multivariable modelling of factors associated with criminal convictions among people experiencing homelessness and serious mental illness: a multi-year study
Bibliographic record
Abstract
People experiencing homelessness and serious mental illness exhibit high rates of criminal justice system involvement. Researchers have debated the causes of such involvement among people experiencing serious mental illness, including what services to prioritize. Some, for example, have emphasized mental illness while others have emphasized poverty. We examined factors associated with criminal convictions among people experiencing homelessness and serious mental illness recruited to the Vancouver At Home study. Participants were recruited between October 2009 and June 2011. Comprehensive administrative data were examined over the five-year period preceding study baseline to identify risk and protective factors associated with criminal convictions among participants (n = 425). Eight variables were independently associated with criminal convictions, some of which included drug dependence (RR = 1.53; P = 0.009), psychiatric hospitalization (RR = 1.44; P = 0.030), an irregular frequency of social assistance payments (compared to regular payments; 1.75; P < 0.001), and prior conviction (RR = 3.56; P < 0.001). Collectively, findings of the present study implicate poverty, social marginalization, crises involving mental illness, and the need for long-term recovery-oriented services that address these conditions to reduce criminal convictions among people experiencing homelessness and serious mental illness.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".