Introducing responsible innovation in health: a policy-oriented framework
Bibliographic record
Abstract
The scholarship on responsible research and innovation (RRI) aims to align the processes and outcomes of innovation with societal values by involving a broad range of stakeholders from a very early stage. Though this scholarship offers a new lens to consider the challenges new health technologies raise for health systems around the world, there is a need to define the dimensions that specifically characterise responsible innovation in health (RIH). The present article aims to introduce an integrative RIH framework drawing on the RRI literature, the international literature on health systems as well as specific bodies of knowledge that shed light on key dimensions of health innovations. Combining inductive and deductive theory-building strategies and concomitant with the development of a formal tool to assess the responsibility of innovations, we developed a framework that is comprised of nine dimensions organised within five value domains, namely population health, health system, economic, organisational and environmental. RIH provides health and innovation policy-makers with a common framework that supports the development of innovations that can tackle significant system-level challenges, including sustainability and equity.
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 | 0.067 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.092 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.012 | 0.010 |
| 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".