Exploring technological innovation in health systems: Is Canada measuring up?
Bibliographic record
Abstract
The societal and economic benefits of technological innovations are indisputable. However, the race for knowledge and talent to develop and commercialise health innovations has never been so fierce. Countries traditionally seen as leaders in health innovation — countries such as the UK — are being challenged by newer players. This study examines how technological innovation is encouraged, and discouraged, in Canada and other selected Organisation for Economic Cooperation and Development (OECD) countries, including the UK, France and the USA. The research uses The Conference Board of Canada's Innovation Framework as an analytical tool in benchmarking the performance of Canada and other OECD countries in several areas of health innovation, including the innovation environment, and the creation, diffusion, transformation and use of knowledge. The results of this study are discouraging for Canada as it scores poorly in many important areas of technological health innovation. Substantial efforts are needed, and needed now, to revitalise health innovation systems and to refuel the capacity to commercialise health innovations. Action in four key areas is recommended.
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 | 0.016 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.025 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".