Business school teams up with clinical innovators: An opportunity for health system transformation
Bibliographic record
Abstract
Healthcare represents one of the largest sectors in the economy with the health spending on average accounting for about 9% of GDP in OECD countries. Canada was projected in 2018 to spend about 11% of its GDP on healthcare with an expected health expenditure growth of 4.2%. Addressing this issue asks for a redesign of health delivery system and associated cultural shift allowing for incorporation of industry and business best practices. To make this redesign happen, system transformation requires seeking out new institutional mechanisms, partnerships, and forums where industry leaders in business and healthcare can develop a top-down approach with a shared vision, shared best practices, and support coming from a bottom-up approach through pilots and scaling-up initiatives. In this article, we describe one successful partnership initiative-Telfer Health Transformation Exchange at the Telfer School of Management at the University of Ottawa.
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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 | 0.073 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.016 |
| Scholarly communication | 0.035 | 0.018 |
| Open science | 0.003 | 0.046 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".