Developing early intervention services in the NHS: a survey to guide workforce and training needs
Bibliographic record
Abstract
Aims and Method We conducted a questionnaire study to establish the incidence, specialist staff availability, treatment provision and socio-demographic profile of patients with first-episode psychosis referred to all adult and child and adolescent community mental health teams in south and west London. Results All 39 teams completed the questionnaire, identifying 295 cases of first-episode psychosis (annual incidence 21/100 000/year) referred in the year 2000. Teams manage to engage most patients with first-episode psychosis. A total of 73% of cases of first-episode psychosis were on some form of Care Programme Approach. However, many teams did not have adequately trained staff to provide psychosocial interventions. Even where such staff were available, care was focused mainly on monitoring medication and risk assessment, with only half the teams providing psycho-educational programmes and only a quarter offering individual cognitive–behavioural therapy to those with first-episode psychosis. Clinical Implications Establishing early intervention services nationwide will require significant new resources, including specialist trained staff, which could prove difficult to provide in inner-city areas. Rather than a single, uniform service model, several models of early intervention services based on locally determined need might be more realistic and appropriate, and also allow research into their relative efficacy.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".