Care management and the care programme approach: towards integration in old age mental health services
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between care management arrangements and the Care Programme Approach (CPA) in the context of old age mental health services and, particularly, dementia services. METHOD: The information reported is from a national study of care management arrangements, funded by the Department of Health. A response rate of 77% was obtained from local authority social services departments. RESULTS: In old age mental health services over half of the respondents reported joint screening arrangements for health and social care, almost four-fifths reported both joint criteria for the allocation of key workers and a clear definition of monitoring responsibilities. Of the latter over two-fifths were reported as being the same in care management and the CPA. Forty-six per cent of respondents provided a specialist service for people with dementia. Three-fifths of respondents reported that they did not apply CPA to people with dementia who were in receipt of care management or did so in less than 20% of cases. Where the CPA was applied it was more likely that a priority would be accorded to care management. A quarter of respondents reported the shared use of assessment documentation for people with dementia. DISCUSSION: The findings are set in the context of service developments to date and the implementation of the two systems of community based coordinated care for older people with mental health problems. Inter-authority variations are noted and the potential for greater service integration within the current legislative framework assessed.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".