Co-creating a new Charter for equitable and inclusive co-creation: insights from an international forum of academic and lived experience experts
Bibliographic record
Abstract
BACKGROUND: Co-creation approaches, such as co-design and co-production, aspire to power-sharing and collaboration between service providers and service users, recognising the specific insights each group can provide to improve health and other public services. However, an intentional focus on equity-based approaches grounded in lived experience and epistemic justice is required considering entrenched structural inequities between service-users and service-providers in public and institutional spaces where co-creation happens. OBJECTIVES: This paper presents a Charter of tenets and principles to foster a new era of 'Equity-based Co-Creation' (EqCC). METHODS: The Charter is based on themes heard during an International Forum held in August 2022 in Ontario, Canada, where 48 lived experience experts and researchers were purposively invited to deliberate challenges and opportunities in advancing equity in the co-creation field. RESULTS: The Charter's seven tenets-honouring worldviews, acknowledging ongoing and historical harms, operationalising inclusivity, establishing safer and brave spaces, valuing lived experiences, 'being with' and fostering trust, and cultivating an EqCC heartset/mindset-aim to promote intentional inclusion of participants with intersecting social positions and differing historic oppressions. This means honouring and foregrounding lived experiences of service users and communities experiencing ongoing structural oppression and socio-political alienation-Black, Indigenous and people of colour; disabled, Mad and Deaf communities, women, 2S/LGBTQIA+ communities, people perceived to be mentally ill and other minoritised groups-to address epistemic injustice in co-creation methodologies and practice, thereby providing opportunities to begin to dismantle intersecting systems of oppression and structural violence. CONCLUSIONS: Each Charter tenet speaks to a multilayered, multidimensional process that is foundational to shifting paradigms about redesigning our health and social systems and changing our relational practices. Readers are encouraged to share their reactions to the Charter, their experiences implementing it in their own work, and to participate in a growing international EqCC community of practice.
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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 | 0.064 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.033 | 0.060 |
| Scholarly communication | 0.035 | 0.018 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".