Best Practice Guidance for Creation and Management of Innovations in Health care and Information and Communications Technologies
Bibliographic record
Abstract
Governments and publics in Europe and around the world have turned to innovation in response to the manifold economic, environmental, and societal challenges we are facing. However, innovations often end up in what is popularly termed as the "valley of death" between upstream creation and downstream product development and implementation. Consequently, the benefits of innovation do not always reach the citizens. In addition, critically informed governance of innovations matter because it allows steering of innovations in response to the values and end points desired by society. With the COVID-19 pandemic, we have witnessed the rise of digital health and new information and communications technologies (ICTs). The pandemic underscored the need for innovation governance between global North and the global South. We report and discuss, in this study, the development of the innXchange innovation wheel to improve innovation creation and management, using a case study of cooperation between Europe and Africa. The innovation wheel offers best practice guidance and framework to build capacity for innovation dimensions such as partnership mobilization, evaluation, and monitoring, not to mention innovation literacy. The framework emphasizes active engagement of all key stakeholders from the very beginning, also referred to as "systematic early dialog." We propose the incorporation of systematic early dialog as the best practice guidance in global South and global North cooperation for health care and ICT innovation. The framework is a novel instrument to help overcome the current barriers in planetary health innovation management and consequently, bring breakthrough discoveries in ICTs and innovative ideas to the people.
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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 | 0.154 | 0.204 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.026 | 0.018 |
| Open science | 0.010 | 0.021 |
| Research integrity | 0.038 | 0.022 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".