From positive screen to engagement in treatment: a preliminary study of the impact of a new model of care for prisoners with serious mental illness
Bibliographic record
Abstract
BACKGROUND: The high prevalence of serious mental illness (SMI) in prisons remains a challenge for mental health services. Many prisoners with SMI do not receive care. Screening tools have been developed but better detection has not translated to higher rates of treatment. In New Zealand a Prison Model of Care (PMOC) was developed by forensic mental health and correctional services to address this challenge. The PMOC broadened triggers for referrals to mental health teams. Referrals were triaged by mental health nurses leading to multidisciplinary team assessment within specified timeframes. This pathway for screening, referral and assessment was introduced within existing resources. METHOD: The PMOC was implemented across four prisons. An AB research design was used to explore the extent to which mentally ill prisoners were referred to and accepted by prison in-reach mental health teams and to determine the proportion of prison population receiving specialist mental health care. RESULTS: The number of prisoners in the study in the year before the PMOC (n = 9,349) was similar to the year after (n = 19,421). 24.6 % of prisoners were screened as per the PMOC in the post period. Referrals increased from 491 to 734 in the post period (Z = -7.23, p < 0.0001). A greater number of triage assessments occurred after the introduction of the PMOC (pre = 458; post = 613, Z = 4.74, p < 0.0001) leading to a significant increase in the numbers accepted onto in-reach caseloads (pre = 338; post = 426, Z = 3.16, p < 0.01). Numbers of triage assessments completed within specified time frames showed no statistically significant difference before or after implementation. The proportion of prison population on in-reach caseloads increased from 5.6 % in the pre period to 7.0 % in the year post implementation while diagnostic patterns did not change, indicating more prisoners with SMI were identified and engaged in treatment. CONCLUSIONS: The PMOC led to increased prisoner numbers across screening, referral, treatment and engagement. Gains were achieved without extra resources by consistent processes and improved clarity of professional roles and tasks. The PMOC described a more effective pathway to specialist care for people with SMI entering prison.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".