Impact of an assertive community treatment model of care on the treatment of prisoners with a serious mental illness
Bibliographic record
Abstract
OBJECTIVES: This study aims to describe the impact of a mental health assertive community treatment prison model of care (PMOC) on improving the ability to identify prisoner needs, provide interventions and monitor their efficacy. METHODS: We carried out a file review across five prisons of referrals in the year before the implementation of the PMOC in 2010 ( n = 423) compared with referrals in the year after ( n = 477). RESULTS: Some improvements in the identification of needs and providing interventions were detected. There was increased use of medication management and clinically significant improvement in addressing engagement with families. Monthly multi-disciplinary team face-to-face contact improved. CONCLUSIONS: Meeting the needs of mentally ill prisoners is challenged by the complexity of the custodial environment. Improvements made resulted from changing the model of care, rather than adding new resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".