What Does Success Look Like in the Forensic Mental Health System? Perspectives of Service Users and Service Providers
Bibliographic record
Abstract
Outcomes research in forensic mental health (FMH) has concentrated on reoffending as the principal indicator of success. Defining success in one-dimensional, negative terms can create a distorted view of the diverse objectives of the FMH system. This qualitative study examined the complexity of success from the perspectives of people in the FMH system. Interviews were conducted with 18 forensic service users and 10 forensic service providers. Data were analyzed inductively using thematic analysis to identify predominant themes. The participants conceptualized success as a dynamic process materializing across six different domains in the context of the FMH system: (a) normal life, (b) independent life, (c) compliant life, (d) healthy life, (e) meaningful life, and (f) progressing life. The results indicate that people who provide or use FMH services emphasize a broad range of processes and outcomes, apart from public safety, when they think about success.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".