Organisational change to integrate self‐management into specialised mental health services: Creating collaborative spaces
Bibliographic record
Abstract
INTRODUCTION: Self-management support for schizophrenia has become expected practice leaving organisations to find ways for feasible implementation. Self-management support involves a foundational cultural shift for traditional disease-based services, new ways of clients-providers working together, coupled with delivering a portfolio of tools and techniques. A new model of self-management support embedded into traditional case management services, called SET for Health (Self-management Engaging Together for Health), was designed and tailored to make such services meaningfully accessible to clients of a tertiary care centre. This paper describes the proof of concept demonstration efforts, the successes/challenges, and initial organisational changes. METHOD: An integrated knowledge translation approach was selected as a means to foster organisational change grounded in users' daily realities. Piloting the model in two community case management programmes, we asked two questions: Can a model of self-management support be embedded in existing case management and delivered within routine specialised mental health services? What organisational changes support implementation? RESULTS: Fifty-one clients were enroled. Indicators of feasible delivery included 72.5% completion of self-management plans in a diverse sample, exceeding the 44% set minimum; and an attrition rate of 21.6%, less than 51% set maximum. Through an iterative evaluation process, the innovation evolved to a targeted hybrid approach revolving around client goals and a core set of co-created reference tools, supplemental tools and resources. Operationalisation by use of tools was implemented to create spaces for client-provider collaborations. Monitoring of organisational changes identified realignment of practices. Changes were made to procedures and operations to further spread and sustain the model. CONCLUSION: This study demonstrated how self-management support can be implemented, within existing resources, for routine delivery of specialised services for individuals living with schizophrenia. The model holds promise as a hybrid option for supporting clients to manage their own health and wellness.
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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 | 0.026 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".