Designing a forensic mental health service delivery model: a multi-professional approach
Bibliographic record
Abstract
Purpose Forensic mental health programs (FMHPs) in Ontario, Canada provide rehabilitation and supervision services. However, models available to guide their delivery are primarily adapted from fields outside of forensic mental health. To partially fill this gap, this paper aims to provide a general review of the process a multi-professional team took to develop the Integrated Forensic Program [IFP]-Ottawa Model of Risk Management & Recovery. Design/methodology/approach Working groups were initiated to identify the needs of patients in their local setting, conduct a literature review on care delivery models in forensic mental health and build a service delivery model specific to forensic mental health. Findings The resulting model places patient engagement at its centre and encompasses eight domains of need that contribute towards the patient’s recovery and the management of the safety risk they pose to the public, namely, the basic needs, diversity and spirituality, social, occupational, psychological, substance use, physical health and mental health domains. Practical implications The IFP-Ottawa Model of Risk Management & Recovery provides a framework to which therapeutic group services for persons in FMHPs can be aligned. Originality/value The leadership teams in FMHPs could use this framework and the method used for its development to ensure group services provided at their FMHPs are evidence-informed and coincide with their patients’ specific needs.
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 | 0.028 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".