Monthly engagement with EIP keyworkers was associated with a five-fold increase in the odds of engagement with psychosocial interventions
Bibliographic record
Abstract
BACKGROUND: Early intervention in psychosis (EIP) supports people who are experiencing their first episode of psychosis (FEP). A new Model of Care (MoC) for EIP services was launched in Ireland in 2019. Three EIP demonstration sites were chosen to test this MoC through a 'hub and spoke' approach. These services were a new way of organising care for people experiencing FEP, based upon a recovery model of care, and which sought to standardise care, improve access by clinically led multidisciplinary teams. This included newly created EIP keyworker roles whereby keyworkers assumed responsibilities regarding assessment, comprehensive individual care planning and coordination of care. METHODS: A mixed methods design utilising the UK Medical Research Council's process evaluation framework. Purposive sampling techniques were utilised. Descriptive analyses and logistic regression were performed to examine how increased keyworker engagement influenced the use of other psychosocial interventions within the EIP demonstration sites. Thematic analyses was used for qualitative data. RESULTS: There was a strong positive relationship between keyworker contacts and psychosocial interventions offered. Specifically, the odds of achieving at least monthly engagement with cognitive behavioural therapy for psychosis (CBTp; (5.76 (2.43-13.64), p < 0.001), and behavioural family therapy (BFT; (5.52(1.63-18.69, p < 0.006)) increased by fivefold with each additional monthly keyworker contact. For individual placement support (IPS) each additional monthly keyworker contact was associated with a three-fold increase in the odds of achieving monthly attendance with IPS (3.73 (1.64-8.48), p < 0.002). Qualitative results found that the EIP keyworker role as viewed by both service users and staff as a valuable nodal point, with a particular emphasis on care coordination and effective communication. CONCLUSIONS: This study advances the understanding of keyworker effects through qualitative evidence of keyworkers functioning as a "linchpin" to the service, while the positive response association between keyworker contacts and engagement with other services provides quantitative support for keyworkers reducing the organisational or structural barriers to service access. Given the importance of these positions, health systems should ensure that EIP programmes identify qualified and experienced staff to fill these roles, as well as allocate the appropriate funding and protected time to support keyworker engagement and impact.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".