The impact of early intervention psychosis services on hospitalisation experiences: a qualitative study with young people and their carers
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,018 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».