The impact of early intervention psychosis services on hospitalisation experiences: a qualitative study with young people and their carers
Bibliographic record
Abstract
Abstract Background While a core aim of early intervention psychosis services (EIPS) is to prevent hospitalisation, many with a first episode of psychosis (FEP) will require inpatient care. We explored young people’s (YP) and their carers’ hospitalisation experiences prior to and during EIPS engagement and how factors across these services influenced these experiences. Methods Using purposive sampling, we recruited twenty-seven YP, all of whom had been involved with the hospital system at some stage, and twelve support persons (parents and partners of YP) from state and federally funded EIPS in Australia with different models of care and integration with 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. Results Four key themes were identified as influential in shaping participant’s hospital experiences and provide ideas for an approach to care that is improved by the effective coordination of that care, and includes this care being delivered in a trauma informed manner: (1) A two-way street: EIPS affected how participants experienced hospitalisation, and vice versa; (2) It’s about people: the quality and continuity of relationships participants had with staff, in hospital and at their EIPS, was central to their experience; (3) A gradual feeling of agency: participants viewed EIPS as both reducing involuntary care and supporting their self-management; and (4) Care coordination as navigation for the healthcare system: great when it works; frustrating when it breaks down. Conclusions Hospitalisation was viewed as a stressful and frequently traumatic event, but a approach to care founded on trust, transparency, and collaboration that is trauma-informed ameliorated this negative experience. Consistent EIPS care coordination was reported as essential in assisting YP and carers navigate the hospital system; conversely, discontinuity in EIPS staff and lack of integration of EIPS with hospital care undermined the positive impact of the EIPS care coordinator during hospitalisation. Care coordinator involvement as a facilitator, information provider, and collaborator in inpatient treatment decisions may improve the usefulness and meaningfulness of hospital interventions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.018 | 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".