Co-design for stroke intervention development: Results of a scoping review
Bibliographic record
Abstract
BACKGROUND: Co-design methodology seeks to actively engage end-users in developing interventions. It is increasingly used to design stroke interventions; however, limited guidance exists, particularly with/for individuals with stroke who have diverse cognitive, physical and functional abilities. Thus, we describe 1) the extent of existing research that has used co-design for stroke intervention development and 2) how co-design has been used to develop stroke interventions among studies that explicitly used co-design, including the rationale, types of co-designed stroke interventions, participants involved, research methodologies/approaches, methods of incorporating end-users in the research, co-design limitations, challenges and potential strategies reported by researchers. MATERIALS AND METHODS: A scoping review informed by Joanna Briggs Institute and Arksey & O'Malley methodology was conducted by searching nine databases on December 21, 2022, to locate English-language literature that used co-design to develop a stroke intervention. Additional data sources were identified through a hand search. Data sources were de-duplicated, and two research team members reviewed their titles, abstracts and full text to ensure they met the inclusion criteria. Data relating to the research objectives were extracted, analyzed, and reported numerically and descriptively. RESULTS: Data sources used co-design for stroke intervention development with (n = 89) and without (n = 139) explicitly using the term 'co-design.' Among studies explicitly using co-design, it was commonly used to understand end-user needs and generate new ideas. Many co-designed interventions were technology-based (65%), and 48% were for physical rehabilitation or activity-based. Co-design was commonly conducted with multiple participants (82%; e.g., individuals with stroke, family members/caregivers and clinicians) and used various methods to engage end-users, including focus groups and workshops. Limitations, challenges and potential strategies for recruitment, participant-engagement, contextual and logistical and ethics of co-designed interventions were described. CONCLUSIONS: Given the increasing popularity of co-design as a methodology for developing stroke interventions internationally, these findings can inform future co-designed studies.
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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 | 0.206 | 0.403 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.041 | 0.052 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".