Evaluating research co-production: Protocol for the Research Quality Plus for Co-Production (RQ+ 4 Co-Pro) Framework
Bibliographic record
Abstract
Abstract BackgroundResearch co-production is an umbrella term used to describe research users and researchers working together to generate knowledge. Research co-production is used to create knowledge that is relevant to current challenges and to increase uptake of that knowledge into practice, programs, products, and/or policy. Yet, rigorous theories and methods to assess the quality of co-production are limited. Here we describe a framework for assessing the quality of research co-production – Research Quality Plus for Co-Production (RQ+ 4 Co-Pro) – and outline our field test of this approach.MethodsUsing a co-production approach, we aim to field test the relevance and utility of the RQ+ 4 Co-Pro framework. To do so we will recruit participants who have led research co-production projects from the international Integrated Knowledge Translation Research Network. We aim to sample 16 to 20 co-production project leads, assign these participants to dyadic groups (8 to 10 dyads), train each participant in the RQ+ 4 Co-Pro framework using deliberative workshops and oversee a simulation assessment exercise using RQ+ 4 Co-Pro within dyadic groups. To study this experience, we use a qualitative design to collect participant demographic information and project demographic information, and will use in-depth semi-structured interviews to collect data related to the experience each participant has using the RQ+ 4 Co-Pro framework.DiscussionThis study will yield knowledge about a new way to assess research co-production. Specifically, it will address the relevance and utility of using RQ+ 4 Co-Pro, a framework that includes context as an inseparable component of research, identifies dimensions of quality matched to the aims of co-production, and applies a systematic and transferable evaluative method for reaching conclusions. This is a needed area of innovation for research co-production to reach its full potential. The findings may benefit co-producers interested in understanding the quality of their work, but also other stewards of research co-production. Accordingly, we undertake this study as a co-production team representing multiple perspectives from across the research enterprise, such as: funders, journal editors, university administrators, and government and health organization leaders.
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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 | 0.266 | 0.392 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.103 | 0.038 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".