Tailoring and Evaluating an Intervention to Support Self-management After Stroke: Protocol for a Multi-case, Mixed Methods Comparison Study
Bibliographic record
Abstract
BACKGROUND: Self-management programs are recognized as a valuable approach to supporting people with long-term conditions, such as stroke, in managing their daily lives. Bridges Self-Management (Bridges) focuses on how practitioners interact and support patients' confidence, skills, and knowledge, and it is an example of a complex intervention. Bridges has been developed and used across multiple health care pathways in the United Kingdom and is theoretically informed by social cognition theory and self-efficacy principles. Evidence shows that self-management programs based on the construct of self-efficacy can be effective. There is still much to learn about how health care services or pathways should implement support for self-management in a sustainable way and whether this implementation process is different depending on the context or culture of the team or service provided. OBJECTIVE: The aim of this study is to tailor and evaluate an intervention (Bridges) to support self-management after stroke in a Swedish context. METHODS: We will use a pretest-posttest design with a case study approach to evaluate the feasibility and implementation of self-management support in two stroke settings. This project includes a complex intervention and depends on the actions of individuals, different contexts, and the adaptation of behavior over time. A mixed methods approach was chosen to understand both outcomes and mechanisms of impact. Data collection will comprise outcome measurements and assessment tools as well as qualitative interviews. Data will be collected concurrently and integrated into a mixed methods design. RESULTS: Recruitment and data collection for the first site of the project ran from September 1, 2021, to January 17, 2022. The intervention at the first site was conducted from November 1, 2021, to March 5, 2022. The evaluation will start after the implementation phase. The second site has been recruited, and the baseline data collection will start in spring 2022. The intervention will start in early autumn 2022. Data collection will be completed by the end of 2022. CONCLUSIONS: This study represents a unique, highly relevant, and innovative opportunity to maximize knowledge and minimize practice gaps in rehabilitation stroke care. The study will produce robust data on the intervention and in-depth data on the contextual factors and mechanisms related to the feasibility of the intervention and for whom it is feasible. Bridges has been used in the United Kingdom for more than 10 years, and this study will explore its contextualization and implementation within a Swedish stroke environment. The evaluation will study results at the patient, staff, and organizational levels and provide recommendations for the adoption and refinement of future efforts to support self-management. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/37672.
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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 | 0.071 | 0.064 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.043 | 0.008 |
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".