Does the Implementation of a Model of Care Improve the Value for Money of Mental Health Services in Prisons?
Bibliographic record
Abstract
BACKGROUND: There is little research into appropriate measures of value for money in prison mental health services. AIMS: To develop and evaluate an accountability framework for an enhanced Prison Model of Care for people with serious mental illness in five New Zealand prisons. A key objective was to identify people with such illnesses who were missing from existing caseloads. METHODS: A generic public sector accountability framework was modified to provide measures of value for money around efficiency in its three component criteria of effectiveness and economy using a pre/post design, allowing measurement of flows between successive stages of this prison healthcare model. Measures were arranged into common dimensions around outcomes, outputs, inputs and costs, varied across the stages. The framework was populated with data collected from five prisons for the pre- and post-implementation periods. RESULTS: Improvements in the three criteria were generally obtained across all five areas of service delivery but especially in the screening, assessment, intervention and reintegration stages. Since these three criteria are major components of value for money, they provide evidence for improvement in value for money of the mental health services in these prisons. Other desired operational changes achieved were a threefold increase in the nurse to doctor ratio at the triage stage and slight increase in doctor to nurse ratio at the treatment stage. Overall, the implementation of this model of care achieved an increase in the size of caseload from 6.1% to 7.3% of the prison muster, equivalent to an increase in caseload of 21%. CONCLUSIONS: This accountability framework confirmed the value for money of the Prison Model of Care for severe mental illness, highlighting areas of good performance as well as areas requiring further development. The framework also provides measures that can be used as key performance indicators in ongoing monitoring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".