How can entrepreneurs experience inform responsible health innovation policies? A longitudinal case study in Canada and Brazil
Bibliographic record
Abstract
AIM: To foster equity and make health systems economically and environmentally more sustainable, Responsible Innovation in Health (RIH) calls for policy changes advocated by mission-oriented innovation policies. These policies focus, however, on instruments to foster the supply of innovations and neglect health policies that affect their uptake. Our study's aim is to inform policies that can support RIH by gaining insights into RIH-oriented entrepreneurs' experience with the policies that influence both the supply of, and the demand for their innovations. METHODS: We recruited 16 for-profit and not-for-profit organisations engaged in the production of RIH in Brazil and Canada in a longitudinal multiple case study. Our dataset includes three rounds of interviews (n = 48), self-reported data, and fieldnotes. We performed qualitative thematic analyses to identify across-cases patterns. FINDINGS: RIH-oriented entrepreneurs interact with supply side policies that support technology-led solutions because of their economic potential but that are misaligned with societal challenge-led solutions. They navigate demand side policies where market approval and physician incentives largely condition the uptake of technology-led solutions and where emerging policies bring some support to societal challenge-led solutions. Academic intermediaries that bridge supply and demand side policies may facilitate RIH, but our findings point to an overall lack of policy directionality that limits RIH. CONCLUSION: As mission-oriented innovation policies aim to steer innovation towards the tackling of societal challenges, they call for a major shift in the public sector's role. A comprehensive mission-oriented policy approach to RIH requires policy instruments that can align, orchestrate, and reconcile health priorities with a renewed understanding of innovation-led economic development.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".