Bibliographic record
Abstract
Canada is one of the highest investors in health on a per capita basis, yet that investment has not necessarily translated into better health for Canadians. Canadians undoubtedly value their healthcare system, but understandably demand better return on their $211 billion investment. The current challenges facing the Canadian healthcare system are numerous, but can be generally grouped into two areas: 1) population-driven challenges, including the ubiquity of chronic conditions and our ever-increasing aging population, and 2) system-driven challenges, including increasing costs of labour in formal healthcare settings and the cost of adopting new medical technologies. Part 1 of this series investigated two thrusts that are taking shape in Canada and around the world to mitigate the challenges described above. The first is an emphasis on decentralizing healthcare by moving care out of resource-intensive institutions (such as hospitals) and into other models of care delivery and even self-management in the home and community. The second focuses on building an integrated healthcare system that uses digital health technologies and processes that connect all parts of the healthcare delivery system, seamlessly, so that critical health information is available when and where it is needed. Introduction | 4 Market Intelligence (MI): What role do startups play in transforming healthcare? Zayna Khayat (ZK): Transforming healthcare requires innovation. Entrepreneurship underpins innovation. Key feeders of this innovation are new health ventures that are founded by entrepreneurs who are challenging the status quo. Entrepreneurship as a mindset is particularly salient in the health sector because we are witnessing the creative destruction of institutions that have been operating for more than 50 years. This report begins with an interview with Zayna Khayat, MaRS Health lead at MaRS Discovery District. It is followed by profiles of 10 Ontario digital health startups that are creating solutions for connectedness and enabling the shift of care from formal settings to the community.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".