Working together effectively in research: Co-design and evaluation of capacity-building modules for researchers and people with lived experience of stroke
Bibliographic record
Abstract
ABSTRACT Objectives To co-design, co-produce and evaluate resources to build the capacity of stroke researchers and people with lived experience of stroke to work together effectively on research projects. Methods Following an interactive workshop, a team of health professional researchers, people with lived experience of stroke, and an education specialist from Stroke Foundation (Australia) convened to co-design and co-produce two learning modules. Mixed methods were used to evaluate the modules. Researchers were invited to complete a survey about their capacity, motivation and intended behaviour before and after accessing the module. Researchers and people with lived experience who accessed the modules were invited to participate in interviews. Survey data were analysed descriptively and pre-and post-module responses were compared using t-tests. Interview data were analysed using qualitative content analysis by three researchers and one survivor of stroke. Results The modules have been widely accessed (researcher module n=1,115 users from 16 countries, lived experience module n=119 users on 20 May 2025). Forty-one researchers completed surveys. Twelve researchers (n=9, 75% women; n=9, 75% from Australia) and 11 people with lived experience of stroke (n=6, 55% women; n=11, 100% from Australia) participated in interviews. Most participants had previous experience of collaborative research. Despite this, participants from both cohorts described a better understanding of roles and types of involvement after completing the modules. Researchers reported significant increases in their likelihood of involving lived experience contributors in research processes after completing the module, compared to responses pre-module. Conclusion Two learning modules co-designed and co-produced by health professional researchers, people with lived experience of stroke, and Stroke Foundation representatives led to improvements in knowledge and confidence for stroke researchers and those with lived experience of stroke to engage in collaborative research. Modules are freely available and compulsory for all researchers submitting Stroke Foundation grant applications.
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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 | 0.104 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".