Recovery and Renewal of Co-Design Approaches in Health: Protocol for a Realist Synthesis
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic significantly transformed the landscape of work and collaboration, impacting design research methodologies and techniques. Co-design approaches have been both negatively and positively affected by the pandemic, prompting a need to investigate and understand the extent of these impacts, changes, and adaptations, specifically in the health sector. Despite the challenges that the pandemic imposed on conducting co-design and related projects, it also encouraged a re-evaluation of co-design practices, leading to innovative solutions and techniques. Designers and researchers have explored alternative ways to engage stakeholders and end users, leveraging digital workshops and participatory digital platforms. These adaptations have the potential to enhance inclusivity, allowing for a wider range of individuals to contribute their perspectives and insights through co-design and thus contribute to healthcare change. OBJECTIVE: This study aims to explore the impacts of the pandemic on co-design and related practices, focusing on co-design practices in healthcare that have been gained, adapted, or enhanced, with a specific focus on issues of equity, diversity, and inclusion. METHODS: The study uses a realist synthesis methodology to identify and analyze the effects of the pandemic on co-design approaches in health, drawing on a range of sources including first-person experiences, gray literature, and academic literature. A community of practice in co-design in health will be engaged to support this process. RESULTS: By examining the experiences and insights of professionals, practitioners, and communities who were actively involved in co-design and have navigated the challenges and opportunities of the pandemic, we can gain a deeper understanding of the strategies, tools, and techniques that have facilitated effective co-design during the pandemic, contributing to building resilience and capacity in co-design in health beyond the pandemic. CONCLUSIONS: By involving community partners, community of practice (research), and design practitioners, we expect closer proximity to practice with capacity building occurring through the realist process, thus enabling rapid adoption and refinement of new techniques or insights that emerge. Ultimately, this research will contribute to the advancement of co-design methodologies and inform the future of co-design in health. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/58318.
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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 | 0.204 | 0.363 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.081 | 0.013 |
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".