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Record W4380576169 · doi:10.1186/s12961-023-00990-y

Evaluating the quality of research co-production: Research Quality Plus for Co-Production (RQ + 4 Co-Pro)

2023· article· en· W4380576169 on OpenAlexafffund
Robert K. D. McLean, Fred Carden, Alice Aiken, Rebecca Armstrong, Judy Bray, Christine Cassidy, Olivia Daub, Erica Di Ruggiero, Leslie A. Fierro, Michelle M. Gagnon, Alison M. Hutchinson, Roman Kislov, Anita Kothari, Sara A. Kreindler, Chris McCutcheon, Jessica Reszel, Gayle Scarrow, Ian D. Graham

Bibliographic record

VenueHealth Research Policy and Systems · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMichael Smith Health Research BCMcGill UniversityCanadian Cancer SocietyUniversity of OttawaPublic Health OntarioUniversity of TorontoWestern UniversityGeorge & Fay Yee Centre for Healthcare InnovationDalhousie UniversityUniversity of ManitobaOttawa HospitalInternational Development Research Centre
FundersCanadian Institutes of Health Research
KeywordsProduction (economics)Quality (philosophy)Relevance (law)Test (biology)Thematic analysisHealth services researchField (mathematics)PsychologyQualitative researchKnowledge managementMedicineComputer sciencePublic healthSociologyNursingPolitical scienceMathematicsSocial science

Abstract

fetched live from OpenAlex

BACKGROUND: Co-production is an umbrella term used to describe the process of generating knowledge through partnerships between researchers and those who will use or benefit from research. Multiple advantages of research co-production have been hypothesized, and in some cases documented, in both the academic and practice record. However, there are significant gaps in understanding how to evaluate the quality of co-production. This gap in rigorous evaluation undermines the potential of both co-production and co-producers. METHODS: This research tests the relevance and utility of a novel evaluation framework: Research Quality Plus for Co-Production (RQ + 4 Co-Pro). Following a co-production approach ourselves, our team collaborated to develop study objectives, questions, analysis, and results sharing strategies. We used a dyadic field-test design to execute RQ + 4 Co-Pro evaluations amongst 18 independently recruited subject matter experts. We used standardized reporting templates and qualitative interviews to collect data from field-test participants, and thematic assessment and deliberative dialogue for analysis. Main limitations include that field-test participation included only health research projects and health researchers and this will limit perspective included in the study, and, that our own co-production team does not include all potential perspectives that may add value to this work. RESULTS: The field test surfaced strong support for the relevance and utility of RQ + 4 Co-Pro as an evaluation approach and framework. Research participants shared opportunities for fine-tuning language and criteria within the prototype version, but also, for alternative uses and users of RQ + 4 Co-Pro. All research participants suggested RQ + 4 Co-Pro offered an opportunity for improving how co-production is evaluated and advanced. This facilitated our revision and publication herein of a field-tested RQ + 4 Co-Pro Framework and Assessment Instrument. CONCLUSION: Evaluation is necessary for understanding and improving co-production, and, for ensuring co-production delivers on its promise of better health.. RQ + 4 Co-Pro provides a practical evaluation approach and framework that we invite co-producers and stewards of co-production-including the funders, publishers, and universities who increasingly encourage socially relevant research-to study, adapt, and apply.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.628
metaresearch head score (Gemma)0.253
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.6280.253
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.009
Science and technology studies0.0200.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0000.001

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 head0.993
GPT teacher head0.885
Teacher spread0.107 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations28
Published2023
Admission routes2
Has abstractyes

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