Research Priorities in Mental Health, Justice, and Safety: A Multidisciplinary Stakeholder Report
Bibliographic record
Abstract
This paper is based on the report following the National Research Agenda Meeting on Mental Health, Justice, and Safety held in Montreal on November 19, 2014, which convened academics; health, social, and legal professionals; and people with lived experience of mental illness from across Canada. The goal was to identify research priorities addressing relevant knowledge gaps and research strategies that can translate into public policy action and improvements in evidence-based services. Participants identified key challenges: (1) inadequate identification and response to needs by civil mental health services and frontline law enforcement, (2) limited specialized resources in forensic and correctional settings, (3) fragmented care and gaps between systems, (4) limited resources for adequate community reintegration, and (5) poor knowledge transfer strategies as obstacles to evidence-based policies. Knowledge gaps were identified in epidemiology and risk reduction, frontline training and programs, forensic and correctional practices, organizations and institutions, knowledge transfer, and rehabilitation. Finally, participants identified potential sources of support to conduct real time research with regard to data collection and sharing. The findings represent a roadmap for how forensic mental health systems can best proceed to address current challenges through research and practice initiatives, drawing from lived, clinical and research experiences of a multidisciplinary group of experts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".