Evaluation of research co-design in health: a systematic overview of reviews and development of a framework
Bibliographic record
Abstract
BACKGROUND: Co-design with consumers and healthcare professionals is widely used in applied health research. While this approach appears to be ethically the right thing to do, a rigorous evaluation of its process and impact is frequently missing. Evaluation of research co-design is important to identify areas of improvement in the methods and processes, as well as to determine whether research co-design leads to better outcomes. We aimed to build on current literature to develop a framework to assist researchers with the evaluation of co-design processes and impacts. METHODS: A multifaceted, iterative approach, including three steps, was undertaken to develop a Co-design Evaluation Framework: 1) A systematic overview of reviews; 2) Stakeholder panel meetings to discuss and debate findings from the overview of reviews and 3) Consensus meeting with stakeholder panel. The systematic overview of reviews included relevant papers published between 2000 and 2022. OVID (Medline, Embase, PsycINFO), EBSCOhost (Cinahl) and the Cochrane Database of Systematic reviews were searched for papers that reported co-design evaluation or outcomes in health research. Extracted data was inductively analysed and evaluation themes were identified. Review findings were presented to a stakeholder panel, including consumers, healthcare professionals and researchers, to interpret and critique. A consensus meeting, including a nominal group technique, was applied to agree upon the Co-design Evaluation Framework. RESULTS: A total of 51 reviews were included in the systematic overview of reviews. Fifteen evaluation themes were identified and grouped into the following seven clusters: People (within co-design group), group processes, research processes, co-design context, people (outside co-design group), system and sustainment. If evaluation methods were mentioned, they mainly included qualitative data, informal consumer feedback and researchers' reflections. The Co-Design Evaluation Framework used a tree metaphor to represent the processes and people in the co-design group (below-ground), underpinning system- and people-level outcomes beyond the co-design group (above-ground). To evaluate research co-design, researchers may wish to consider any or all components in the tree. CONCLUSIONS: The Co-Design Evaluation Framework has been collaboratively developed with various stakeholders to be used prospectively (planning for evaluation), concurrently (making adjustments during the co-design process) and retrospectively (reviewing past co-design efforts to inform future activities).
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 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 | 0.459 | 0.527 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.023 | 0.022 |
| Bibliometrics | 0.062 | 0.040 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.009 | 0.019 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".