"Tools to Live": Using Community-Engaged Scholarship to Assess the Role of a Canadian Non-Profit Organization in Serving Persons with Mental Health Issues and Concurrent Disorders
Bibliographic record
Abstract
Ample empirical evidence highlights the significant prevalence of persons with mental health issues and concurrent disorders involved in the criminal justice system. This population frequently uses services provided by community based non-profit organizations for aid and support. Yet, there is a dearth of research that addresses the prevalence of mental health issues and concurrent disorders amongst those served by such organizations, and the barriers to community reintegration that they face. Focusing on the prevalence of mental health issues and concurrent disorders among individuals involved or at risk of involvement in the criminal justice system, the John Howard Society of Waterloo-Wellington and the University of Guelph embarked upon a mixed-methods community-engaged research partnership. Information for the clients in the ‘Community Aftercare’ program was collected in relation to mental health issues and concurrent disorders. Our data reveal the ‘typical’ profile of the clients accessing the Aftercare program, which includes a high prevalence of mental health issues and concurrent disorders. Further, interviews identified stigma, history of victimization, complex needs, criminal history, diminished welfare state, and a lack of social support/social capital as barriers faced by the Aftercare clients. The Aftercare program, however, acts as a form of social support and builds social capital for the clients. A lack of adequate funding to address the complex needs of the Aftercare clients was identified as a program limitation, and the findings revealed a need for additional funding.
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".