Examining the use of the recovery model with individuals found not criminally responsible on account of mental disorder
Bibliographic record
Abstract
In providing the care and control of individuals found Not Criminally Responsible on Account of Mental Disorder (NCRMD), forensic psychiatry attempts to balance the protection of society with the treatment of mental illness. A new approach in mental health care is the recovery model, which centers on the understanding that there should be a ‘recovery in’, not a ‘recovery from’ serious mental illness. In clinical practice, this means that treatment decisions should be made in collaboration with patients and include their personal circumstances, such as criminality and aspirations. Concepts that intersect with these goals are elements like choice, hope, personal responsibility, and empowerment. This paper examines the implementation of the recovery model in forensic mental health settings and provides an in-depth exploration and evaluation of the model as it is practiced at a forensic psychiatric outpatient clinic with individuals found NCRMD. Ten participants, including both individuals found NCRMD and psychiatric professionals, took part in semi-structured interviews. The paper examines the experiences, perceptions and challenges of implementing the recovery model in a forensic psychiatric setting and compares its strategies to the predominant risk-based forensic practices. The analysis suggests that it is difficult to implement the recovery model in a forensic setting without compromising either the recovery model or the risk management approach. Keywords: NCRMD, recovery model, risk management, outpatient setting, qualitative, forensic mental health
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| 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".