Transforming Care Through Co-Design: Developing Inclusive Caregiver-Centered Education in Healthcare
Bibliographic record
Abstract
Background: Family caregivers provide most (75–90%) of the essential unpaid care and support for individuals living with chronic conditions, disabilities, and age-related needs in the community, with about half performing medical tasks traditionally performed by professionals. Caregivers also assist with 15 to 35% of the care in congregate care settings. Yet despite their critical contributions to patient care, caregivers face stress, declining well-being, and insufficient recognition in healthcare systems. Addressing these challenges requires innovative, person-centered approaches to training healthcare providers. Co-design or co-production are participatory research methods that involve individuals with lived experience to ensure relevance and impact. Objective: This study sought to understand how participatory co-design principles influenced learning, collaboration, and engagement among diverse participants in developing a caregiver-centered education program for healthcare providers. Actionable recommendations for optimizing co-design processes are provided. Methods: Eighty-five participants from a team of 155 collaborators, including caregivers, healthcare providers, educators, policymakers, and leaders, participated in ten focus group sessions conducted in Zoom breakout rooms. Interviews were transcribed verbatim and analyzed using Thorne’s interpretive description and Braun and Clarke’s reflexive thematic analysis. Results: Participants described the co-design process as fostering collaboration, inclusivity, and skill enhancement. Exposure to diverse perspectives expanded transformative understanding and prompted reflection on caregiver support within professional practices. Skilled facilitation ensured equitable engagement. Challenges included information overload and personal time constraints. Participants liked using breakout rooms to mitigate the dynamics of large group management. Still, they recommended pre-meeting materials, flexible scheduling, and expanding stakeholder diversity (e.g., rural, Indigenous, and immigrant caregivers). Conclusions: Co-design fosters meaningful, caregiver-centered education through collaboration and inclusivity. Addressing logistical challenges and representation gaps can further enhance the impact of co-design and empower multi-level, interdisciplinary partners to inform equitable healthcare education.
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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 | 0.038 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".