Integrating Self-management Support into Care - An Engaging Evidence-Based Option for Thriving with Schizophrenia
Bibliographic record
Abstract
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.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".