The dark side of coproduction: do the costs outweigh the benefits for health research?
Bibliographic record
Abstract
BACKGROUND: Coproduction, a collaborative model of research that includes stakeholders in the research process, has been widely advocated as a means of facilitating research use and impact. We summarise the arguments in favour of coproduction, the different approaches to establishing coproductive work and their costs, and offer some advice as to when and how to consider coproduction. DEBATE: Despite the multiplicity of reasons and incentives to coproduce, there is little consensus about what coproduction is, why we do it, what effects we are trying to achieve, or the best coproduction techniques to achieve policy, practice or population health change. Furthermore, coproduction is not free risk or cost. Tensions can arise throughout coproduced research processes between the different interests involved. We identify five types of costs associated with coproduced research affecting the research itself, the research process, professional risks for researchers and stakeholders, personal risks for researchers and stakeholders, and risks to the wider cause of scholarship. Yet, these costs are rarely referred to in the literature, which generally calls for greater inclusion of stakeholders in research processes, focusing exclusively on potential positives. There are few tools to help researchers avoid or alleviate risks to themselves and their stakeholders. CONCLUSIONS: First, we recommend identifying specific motivations for coproduction and clarifying exactly which outcomes are required for whom for any particular piece of research. Second, we suggest selecting strategies specifically designed to enable these outcomes to be achieved, and properly evaluated. Finally, in the absence of strong evidence about the impact and process of coproduction, we advise a cautious approach to coproduction. This would involve conscious and reflective research practice, evaluation of how coproduced research practices change outcomes, and exploration of the costs and benefits of coproduction. We propose some preliminary advice to help decide when coproduction is likely to be more or less useful.
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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 | 0.373 | 0.525 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.009 | 0.069 |
| Scholarly communication | 0.035 | 0.065 |
| Open science | 0.008 | 0.024 |
| Research integrity | 0.017 | 0.019 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".