Using Co-Design and Engagement Principles for Health System Transformation: Our Lessons Learned
Bibliographic record
Abstract
Introduction: The Middlesex London Ontario Health Team (ML OHT), comprised of multiple health and social care partners, aims to support the primary and secondary healthcare needs of people living in London-Middlesex, Ontario, Canada. Co-design and engagement approaches have been intentionally and carefully embedded in all levels of the OHT work. The OHT applies co-design and engagement principles through the co-leadership of patient/caregiver partners on governing committees and strategic planning, the involvement of patients/caregivers on project implementation and evaluation working groups, and through the development of a patient/caregiver council. Through relationships with local patients/clients, caregiver partners, providers, and health system administrators, co-design processes are being used to collectively identify, develop, implement, and evaluate health system improvement strategies. Partnerships with patients and caregivers are guided by an equity, diversity, and inclusion matrix to ensure patient/caregiver representation aligns with the demographics of our local population. Workshop Purpose: Over our two-year journey, we have learned a lot about co-design and engagement practices, and how to embed approaches into an integrated health system. Embedding engagement practices into every level of the structure, and every stream of work, is an ongoing, continuous learning journey. The ML OHT has recently focused on building partnerships with underserved and underrepresented populations. Through this work, the team has adjusted engagement practices to align with the needs and desires of specific populations. During this workshop, we hope the audience can learn from our experiences, and share some of their own insights and learnings. Short Statement: Embedding co-design and engagement practices into an integrated health system structure from bottom to top, is an ongoing, continuous learning journey. ML OHT would like to share lessons learned and hear from the audience about ways to adjust practices for specific populations. Objectives and Workshop Structure: The interactive workshop structure and objectives, co-presented by ML OHT team members (staff members and patient/caregiver partner), will include: 1. Introduction and Background (25mins) - i) Share our co-design structure and framework, ii) Discuss how we evaluate our co-design participation and identify gaps; iii) Review specific engagement strategies for different groups/populations; iv) Describe how to apply co-design principles and findings to strategy/program development and operations; and, 2. Interactive Case Study (20mins) - v) Conduct an interactive case study exercise where audience members can share their own experiences and build a custom engagement strategy for unique stakeholder groups; and 3. Sharing Learnings and Closing (15mins) Audience and Engagement: This interactive workshop would be appropriate for anyone with a role in system improvement (including providers, patients/caregivers, leaders, researchers) with various levels of co-design experiences, from novice to expert. Key Messages: Following the presentation, participants will leave with the following messages: i) meaningful co-design takes time and is a continuous learning journey, ii) consideration of co-design approaches is needed at all levels of a strategy (e.g. on-the-ground implementation work, up to the governance table), iii) evaluation and ongoing assessment of co-design participation is necessary to ensure appropriate engagement, and iv) co-design approaches should be appropriately adjusted to meet the needs of the population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.094 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.033 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".