Creative processes in co-designing a co-design hub: towards system change in health and social services in collaboration with structurally vulnerable populations
Bibliographic record
Abstract
Background: Co-design is an approach to engaging stakeholders in health and social system change that is rapidly gaining traction, yet there are also questions about the extent to which there is meaningful engagement of structurally vulnerable communities and whether co-design leads to lasting system change. The McMaster University Co-Design Hub with Vulnerable Populations Hub (‘the Hub’) is a three-year interdisciplinary project with the goal of facilitating partnerships, advancing methods of co-design with vulnerable populations, and mobilising knowledge. Aims and objectives: A developmental evaluation approach inspired by experience-based co-design was used to co-produce a theory of change to understand how the co-design process could be used to creatively co-design a co-design hub with structurally vulnerable populations. Methods: Twelve community stakeholders with experience participating in a co-design project were invited to participate in two online visioning events to co-develop the goals, priorities, and objectives of the Hub. Qualitative data were analysed using a thematic content analysis approach. Findings: A theory of change framework was co-developed that outlines a future vision for the Hub and strategies to achieve this, and a visual graphic is presented. Discussion and conclusions: Through critical reflection on the work of the Hub, we focus on the co-creative methods that were applied when co-designing the Hub’s theory of change. Moreover, we illustrate how co-creative processes can be applied to embrace the complexity and vulnerability of all stakeholders and plan for system change with structurally vulnerable populations.
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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 | 0.090 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.017 | 0.039 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".