User Participation in Coproduction of Health Innovation: Proposal for a Synergy Project
Bibliographic record
Abstract
BACKGROUND: This project concerns advancing knowledge, methods, and logic for user participation in coproduction of health innovations. Such advancement is vital for several reasons. From a user perspective, participation in coproduction provides an opportunity to gain real influence over goal definition, design, and implementation of health innovations, ensuring that the solution developed solves real problems in right ways. From a societal perspective, it's a mean to improve the efficiency of health care and the implementation of the Patient Act. As for industry, frameworks and knowledge of coproduction offer tools to operate in a complex sector, with great potential for innovation of services and products. OBJECTIVE: The fundamental objective of this project is to advance knowledge and methods of how user participation in the coproduction of health innovations can be applied in order to benefit users, industry, and public sector. METHODS: This project is a synergy project, which means that the objective will be accomplished through collaboration and meta-analysis between three subprojects that address different user groups, apply different strategies to promote human health, and relate to different parts of the health sector. Furthermore, subprojects focus on distinctive stages in the spectrum of innovation, with the objective to generate knowledge of the innovation process as a whole. The project is organized around three work packages related to three challenges-coproduction, positioning, and realization. Each subproject is designed such that it has its own field of study with clearly identified objectives but also targets work packages to contribute to the project as a whole. The work on the work packages will use case methodology for data collection and analysis based on the subprojects as data sources. More concretely, logic of multiple case studies will be applied with each subproject representing a separate case which is similar to each other in its attention to user participation in coproduction, but different regarding, for example, context and target groups. At the synergy level, the framework methodology will be used to handle and analyze the vast amount of information generated within the subprojects. RESULTS: The project period is from July 1, 2018 to June 30, 2022. CONCLUSIONS: By addressing the objective of this project, we will create new knowledge on how to manage challenges to health innovation associated with the coproduction process, the positioning of solutions, and realization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".