Key Worker–Mediated Enhancement of Physical Health in First Episode Psychosis: Protocol For a Feasibility Study in Primary Care
Bibliographic record
Abstract
BACKGROUND: Studies have demonstrated that, for patients with psychosis, a majority of the decline in health status and functioning emerges during the first few years after the onset of illness. This knowledge led to the development of specialized early intervention services (EISs) targeting patients experiencing their first episode of psychosis. The central component of EISs is often assertive case management delivered by a multidisciplinary team, where an appointed key worker is responsible for coordinating treatment and delivering various psychosocial interventions to service users. OBJECTIVE: This paper outlines the protocol for a feasibility study examining how key workers may enhance physical health by supporting integration between primary and secondary care. METHODS: Semistructured interviews were conducted with key stakeholder groups (General Practitioners and health care professionals working in mental health services). The interviews informed the development of the complex intervention involving a longitudinal pre-post intervention in 8 general practices in 2 regions in Ireland (one urban and one rural). Patients with first episode psychosis (FEP) will be identified from clinical records at general practices and mental health services. RESULTS: Baseline and follow-up data (at 6 months) will be collected, examining measures of feasibility, acceptability, and intervention effect size. CONCLUSIONS: Study findings will inform future practice by examining feasibility of key workers enhancing physical health through improved interaction between primary and secondary care. By identifying issues involved in enhancing recruitment and retention, as well as the likely effect size, the study will inform a future definitive intervention. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/13115.
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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 | 0.064 | 0.040 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.071 | 0.017 |
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".