Experiences of service transitions in Australian early intervention psychosis services: a qualitative study with young people and their supporters
Bibliographic record
Abstract
BACKGROUND: Different Early Intervention Psychosis Service (EIPS) models of care exist, but many rely upon community-based specialist clinical teams, often with other services providing psychosocial care. Time-limited EIPS care creates numerous service transitions that have potential to interrupt continuity of care. We explored with young people (YP) and their support people (SP) their experiences of these transitions, how they affected care and how they could be better managed. METHODS: Using purposive sampling, we recruited twenty-seven YP, all of whom had been hospitalised at some stage, and twelve SP (parents and partners of YP) from state and federally funded EIPS in Australia with different models of care and integration into secondary mental health care. Audio-recorded interviews were conducted face-to-face or via phone. A diverse research team (including lived experience, clinician and academic researchers) used an inductive thematic analysis process. Two researchers undertook iterative coding using NVivo12 software, themes were developed and refined in ongoing team discussion. RESULTS: The analysis identified four major service-related transitions in a YP's journey with the EIPS that were described as reflecting critical moments of care, including: transitioning into EIPS; within service changes; transitioning in and out of hospital whilst in EIPS care; and, EIPS discharge. These service-related transition affected continuity of care, whilst within service changes, such as staff turnover, affected the consistency of care and could result in information asymmetry. At these transition points, continuity of care, ensuring service accessibility and flexibility, person centredness and undertake bio-psychosocial support and planning were recommended. State and federally funded services both had high levels of service satisfaction, however, there was evidence of higher staff turnover in federally funded services. CONCLUSION: Service transitions were identified as vulnerable times in YP and SP continuity of care. Although these were often well supported by the EIPS, participants provided illustrative examples for service improvement. These included enhancing continuity and consistency of care, through informed and supportive handovers when staff changes occur, and collaborative planning with other services and the YP, particularly during critical change periods such as hospitalisation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".