Old age mental health services in England: implementing the National Service Framework for Older People
Bibliographic record
Abstract
BACKGROUND: There is much variation in the services provided for older people with mental health problems. In England, the National Service Framework for Older People (NSFOP) sought to address these inconsistencies and improve care. This study describes the situation three years after its publication. METHODS: A postal survey of old age psychiatrists collected data on the NSFOP mental health model: the range of specialist mental health provision, the nature of the specialist:generic service interface and the degree of interdisciplinary/interagency working. RESULTS: Three hundred and eighteen (72%) consultants responded. Considerable differences existed in the deployment of key professionals within community teams, with more than a third lacking ring-fenced social work time. Few services had dedicated rehabilitation beds and nearly a third lacked separate facilities for people with organic and functional illnesses. Increasing numbers of consultants had access to a memory clinic and there was some suggestion that liaison services were developing, but little indication of increased support for care homes. Several services had yet to agree protocols with primary care, or to implement measures promoting effective information-sharing and integrated care, and there was little evidence that the introduction of the Single Assessment Process (SAP) had significantly changed practice. Although just over half of consultants reported that mental health services were improving, less than a quarter considered community provision adequate. CONCLUSIONS: Three years after the publication of the NSFOP there remained significant gaps in services for older people with mental health problems and substantial variation in provision between districts.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".