Reverse innovation: an opportunity for strengthening health systems
Bibliographic record
Abstract
BACKGROUND: Canada, when compared to other OECD countries, ranks poorly with respect to innovation and innovation adoption while struggling with increasing health system costs. As a result of its failure to innovate, the Canadian health system will struggle to meet the needs and demands of both current and future populations. The purpose of this initiative was to explore if a competition-based reverse innovation challenge could mobilize and stimulate current and future leaders to identify and lead potential reverse innovation projects that address health system challenges in Canada. METHODS: An open call for applications took place over a 4-month period. Applicants were enticed to submit to the competition with a $50,000 prize for the top submission to finance their project. Leaders from a wide cross-section of sectors collectively developed evaluation criteria and graded the submissions. The criteria evaluated: proof of concept, potential value, financial impact, feasibility, and scalability as well as the use of prize money and innovation team. RESULTS: The competition received 12 submissions from across Canada that identified potential reverse innovations from 18 unique geographical locations that were considered developing and/or emerging markets. The various submissions addressed health system challenges relating to education, mobile health, aboriginal health, immigrant health, seniors health and women's health and wellness. Of the original 12 submissions, 5 finalists were chosen and publically profiled, and 1 was chosen to receive the top prize. CONCLUSIONS: The results of this initiative demonstrate that a competition that is targeted to reverse innovation does have the potential to mobilize and stimulate leaders to identify reverse innovations that have the potential for system level impact. The competition also provided important insights into the capacity of Canadian students, health care providers, entrepreneurs, and innovators to propose and implement reverse innovation in the context of the Canadian health system.
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 | 0.046 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".