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Record W4411557224 · doi:10.1080/0267257x.2025.2522929

From participants to partners: advancing consumer involvement in transformative research

2025· article· en· W4411557224 on OpenAlexfundno aff
Joan Carlini, E‐J Milne, Elizabeth Kendall, Georgia Tobiano, Rachel Muir

Bibliographic record

VenueJournal of Marketing Management · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilNational Research Foundation of KoreaNational Institute for Health and Care ResearchCanadian Institutes of Health ResearchNational Research FoundationMedical Research Council
KeywordsTransformative learningConsumer researchMarketingBusinessPsychologySociologyDevelopmental psychology

Abstract

fetched live from OpenAlex

This study explores how participatory research, involving consumers as co-researchers, can improve research quality, equity, and accountability. Systemic barriers, limited training, and resource constraints often hinder effective consumer involvement in research, particularly for those experiencing marginalisation and vulnerability. The study examines how participatory research can strengthen consumer involvement in research, build individual agency, and create positive societal impact. Through two qualitative co-design sessions using the LEGO® SERIOUS PLAY® methodology, the study conceptualises the Research Partnership Model for Transformative Impact. This model highlights the challenges faced by researchers and consumers, identifies gaps in expectations, capabilities, well-being outcomes, and calls for stronger university policies, better support, and collaborative strategies to foster more inclusive and equitable research.

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)0.152
metaresearch head score (Gemma)0.122
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.848
Threshold uncertainty score0.803

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1520.122
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0160.024
Scholarly communication0.0180.019
Open science0.0030.043
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0110.002

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.

Opus teacher head0.321
GPT teacher head0.544
Teacher spread0.222 · 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.

Study designTheoretical or conceptual
DomainMethods
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

Citations3
Published2025
Admission routes1
Has abstractyes

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