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Record W4393871604 · doi:10.1111/hex.14041

Advancing a collective vision for equity‐based cocreation through prototyping at an international forum

2024· article· en· W4393871604 on OpenAlexafffundabout
Michelle Phoenix, Sandra Moll, Alexa Vrzovski, Le‐Tien Bhaskar, Samantha Micsinszki, Emma Bruce, Lulwama Mulalu, Puspita Hossain, Bonnie Freeman, Gillian Mulvale

Bibliographic record

VenueHealth Expectations · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsFirst Nations University of CanadaImpactToronto Metropolitan UniversityAssembly of First NationsMcMaster University
FundersSocial Sciences and Humanities Research CouncilMcMaster University
KeywordsEquity (law)Public relationsCitizen journalismPsychologyPreparednessSociologyMedical educationPolitical scienceMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Cocreation has the potential to engage people with lived and living experiences in the design and evaluation of health and social services. However, guidance is needed to better include people from equity-deserving groups (EDGs), who are more likely to face barriers to participation, experience ongoing or historical harm, and benefit from accessible methods of engagement. OBJECTIVE: The aim of this international forum (CoPro2022) was to advance a collective vision for equity-based cocreation. DESIGN: A participatory process of engagement in experiential colearning and arts-based creative and reflective dialogue. Visual prototypes were created and synthesised to generate a collective vision for inclusive equity-based cocreation. SETTING AND PARTICIPANTS: The Forum was held at the Gathering Place by the Grand River in Ohsweken, Ontario, Canada. A total of 48 participants attended the forum. They were purposely invited and have intersecting positionalities (21 academic experts, six experience experts, 10 trainees, and 11 members of EDGs) from nine countries (Bangladesh, Botswana, Canada, England, Italy, Norway, Scotland, Singapore, Sweden). COPRO2022 ACTIVITIES: CoPro2022 was an immersive experience hosted on Indigenous land that encouraged continuous participant reflection on their own worldviews and those of others as participants openly discussed the challenges and opportunities with engaging EDGs in cocreation activities. Visual prototypes and descriptions created in small groups were informed by participants' reflections on the panel presentations at the Forum and their own experiences with equity-based cocreation. Following the event, the authorship team inductively coded themes from the prototype descriptions and met to discuss the cross-cutting themes. These informed the design of an illustrated collective vision for Equity Based Co-Creation (EqCC). RESULTS: Six prototypes were cocreated by each small group to illustrate their vision for EqCC. Within these, four cross-cutting themes were identified: (i) go to where people are, (ii) nurture relationships and creativity, (iii) reflect, replenish and grow, (iv) and promote thriving and transformation. These four themes are captured in the Collective EqCC Vision to guide a new era of inclusive excellence in cocreation activities. PATIENT OR PUBLIC CONTRIBUTION: Service users, caregivers, and people with lived experience were involved in leading the design of the CoPro2022 and co-led the event. This included activities at the event such as presenting, facilitating small and large group discussion, leading art-based activities, and reflecting with the team on the lessons learned. People with lived experience were involved in the analysis and knowledge sharing from this event. Several members of the research team (students and researchers) also identified as members of EDGs and were invited to draw from their personal and academic knowledge.

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 imitation

Not 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.

metaresearch head score (Codex)0.065
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.342

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0650.043
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0100.016
Scholarly communication0.0130.011
Open science0.0030.024
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.051
GPT teacher head0.425
Teacher spread0.374 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2024
Admission routes3
Has abstractyes

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