Staff supported community outings among forensic mental health patients: patient characteristics, rehabilitative goals, and (the absence of) adverse outcomes
Bibliographic record
Abstract
Mental health professionals are tasked with making difficult clinical decisions in treatment settings. In the forensic system, decision making regarding staff supervised community outings (SSCOs) provides a significant challenge due to the need to balance patient liberties, mental health recovery, and public safety. This study explored the characteristics and rehabilitative nature of SSCOs, characteristics of patients attending SSCOs, and any adverse events that occurred during the outings. Employing a cross-sectional design, 110 patients who participated in SSCOs over a one-year period from a Canadian Forensic Psychiatric Hospital were included. Clinical records were reviewed to capture patient and SSCO variables. Descriptive analyses were used to calculate participant, risk, SSCO, and adverse event characteristics. Qualitative analysis was used to explore the purpose of SSCOs and rehabilitative progress that occurred during the outings. Patients attending SSCOs were comprised of long-stay patients with over half having committed a violent index offence. Almost 75% of patients had a moderate/high risk for violence and 50% of the patients had a moderate/high risk of absconding. During the study period, 463 SSCOs were completed. Most outings focused on developing skills for daily living and staff comments suggested many patients developed skills in these areas. Despite considerable risk profiles and public concern regarding forensic patients having community access, there was a single occurrence of unauthorized leave and no instances of violence or substance use. This research can disrupt stigma, demonstrating that SSCOs support a specific rehabilitative intent, promote community reintegration, and maintain public safety.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".