Integrating Self-management Support into Care - An Engaging Evidence-Based Option for Thriving with Schizophrenia
Notice bibliographique
Résumé
Background: People with serious mental illness such as schizophrenia, often experience stigma, marginalization and systems that focus on symptoms, undermining their resilience, or capacity to proactively manage their health condition(s) and live a meaningful life. Co-designing and supporting self-management can address health equity by transforming healthcare into collaborative partnerships, and making the difference between surviving and thriving, and living a life of quality with mental illness. Although self-management support is a Health Quality Ontario quality standard, it is not routine practice. Our aim was to develop and evaluate SET for Health, an accessible model of self-management support embedded in team-based care for people living with schizophrenia and their families directed at health and support networks during pursuit of personal recovery goals. Approach: An integrated knowledge translation approach was selected for sustainable development grounded in clients life challenges and providers working realities, and to benefit from everyone knowledge and experiences. A 2-year mixed methods study, quantitatively nested within a qualitative component, gathered data to understand and evaluate how the model worked in actual practice. Sequential triangulation of data (casebook audits, client and clinician transcripts, practice observations, anecdotal comments, outcome measures, changes in care processes) explored experiences, perceptions and practices to understand the value and impact from users perspectives. Follow-up analysis of hospital utilization and client movement was conducted. Results: In two tertiary, public, mental health services, 0 multidisciplinary providers implemented SET for Health with 5 diverse community dwelling adults with schizophrenia. Accessibility and feasibility were demonstrated by beating industry benchmarks; cutting drop-outs by half and increasing completion rates by 28%. Creation of collaborative learning spaces supported by tools for client voice and shared decision-making was affirmed. Clients valued time and space for self-reflection, gaining perspective; learning about self-management strategies; expanding capability and realize I can do and getting on with life, feeling good about managing life challenges. Providers valued seeing client engagement, progress in recovery; self-management conversations, collaboration, new understandings for both provider and client; an expanded toolbox of strategies, options; and philosophy, framework to structure care around. Statistically significant client benefits pre-post included: illness severity, social and occupational functioning, illness management, functional recovery, and time spent in meaningful roles. Benefits held up regardless of age, education, length of illness, and tenure with provider. Total cost savings of $,949,727 or $5,309 per person was seen from reduced ER visits, rehospitalizations, and hospital days. Clients required less intensity of service delivery. Implications: Self-Management support using the SET for Health approach is a practical, evidence-based, person-centred option for people living with schizophrenia that can be co-designed and delivered in routine care. Support and organizational changes are important for integration and sustainability. Implementation can be transformational for clients lives and well-being, the organization and culture of services, and utilization of resources. We have packaged and begun an evaluation of a remote interdisciplinary training series to increase client access and further study the SET for Health model.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».