Impacts of implementing a forensic treatment mall: A program evaluation
Bibliographic record
Abstract
Psychiatric in-patients need programming and clinical interventions to support recovery and gradual re-integration into the community.Treatment malls are designed to meet these needs by offering psychosocial, person-centred rehabilitation groups in a centralized setting.Evidence suggests this treatment model increases patient engagement, broadens social networks, and improves functioning.Given this, our hospital implemented a treatment mall in our forensic services department in 2018.Because this was a new initiative at our hospital and the lack of outcome data on treatment malls in Canada, we conducted a program evaluation to explore the effects of the treatment mall and identify possible areas of improvement.Guided by a logic model, this evaluation used a retrospective chart review.Variables of interest included patient engagement, psychotropic pro re nata (PRN) administration, recovery scores, time spent off unit, elopement, use of restraints and seclusion, and patient activity levels.We assessed whether these changed post-implementation of the treatment mall.The results provide preliminary evidence of the positive effects of the treatment mall.Patient engagement and time spent off unit increased.Maintenance of changes and long-term outcomes remain to be seen; however, this evaluation provides support for the continued use and investigation of the treatment mall for our forensic services department.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".