A prison mental health in‐reach model informed by assertive community treatment principles: evaluation of its impact on planning during the pre‐release period, community mental health service engagement and reoffending
Bibliographic record
Abstract
BACKGROUND: It is well recognised that prisoners with serious mental illness (SMI) are at high risk of poor outcomes on return to the community. Early engagement with mental health services and other community agencies could provide the substrate for reducing risk. AIM: To evaluate the impact of implementing an assertive community treatment informed prison in-reach model of care (PMOC) on post-release engagement with community mental health services and on reoffending rates. METHODS: One hundred and eighty prisoners with SMI released from four prisons in the year before implementation of the PMOC were compared with 170 such prisoners released the year after its implementation. RESULTS: The assertive prison model of care was associated with more pre-release contacts with community mental health services and contacts with some social care agencies in some prisons. There were significantly more post-release community mental health service engagements after implementation of this model (Z = -2.388, p = 0.02). There was a trend towards reduction in reoffending rates after release from some of the prisons (Z =1.82, p = 0.07). CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: Assertive community treatment applied to prisoners with mental health problems was superior to 'treatment as usual', but more work is needed to ensure that agencies will engage prisoners in pre-release care. The fact that the model showed some benefits in the absence of any increase in resources suggests that it may be the model per se that is effective.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".