Planning effective mental healthcare in prisons: findings from a national consultation on the care programme approach in prisons
Bibliographic record
Abstract
Aims The Care Programme Approach (CPA) can be an effective tool in coordinating the care and treatment needs of people with mental illness and learning disabilities. Within prisons settings, the CPA has been poorly implemented and the principles underpinning this approach have been lost. The aim of this research was to look at the key themes identified as part of a consultation process to develop quality guidance on planning effective mental healthcare in prisons in relation to the CPA. Method The consultation exercises included telephone interviews and hosting a national consultation event to represent the views of prisons nationally. It was conducted by the Quality Network for Prison Mental Health Services, a quality improvement initiative organised by the Royal College of Psychiatrists’ Centre for Quality Improvement. Result The results derived from the consultation process indicates that CPA in prisons is inconsistently adopted and that there is lack of confidence in the process from prison mental health teams, particularly with how to engage community mental health teams. Conclusion This concludes that there is a substantial need for standardisation and consistency in the application of the CPA process within prisons, for the purposes of enhanced care delivery, greater continuity of care, and improved patient outcomes. The Quality Network for Prison Mental Health Services used the findings from this consultation to produce a national guidance document on planning effective mental healthcare in prisons, which can be accessed for free by all prison mental health teams.
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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 | 0.025 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".