Catalyzing Digital Health Innovation in Ontario: The Role of an Academic Medical Centre
Bibliographic record
Abstract
Overcoming barriers to health system innovation is an ongoing challenge in Canada.A total of 51 participants attended a digital health symposium in October 2017 to discuss the role of an academic medical centre (AMC) in advancing innovation.The conversation centred around (i) the current state of innovation in healthcare; (ii) the need for an innovation catalyst; and (iii) the roadmap for an AMC to drive change.AMCs can address the barriers to digital health innovation in Canada by providing a centralized network and infrastructure that supports innovation throughout its journey from "bench to bedside" as well as supporting educational reform. RésuméSurmonter les obstacles à l'innovation dans le système de santé est un défi constant au Canada.En tout, 51 participants ont assisté à un symposium sur la santé numérique, en octobre 2017, pour discuter du rôle des centres médicaux universitaires (CMA) dans la promotion de l'innovation.La conversation a porté sur (i) l'état actuel de l'innovation dans les soins de santé, (ii) le besoin d' un catalyseur d'innovation et (iii) la feuille de route qui permet à un CMA de provoquer le changement.Les CMA peuvent affronter les obstacles à l'innovation numérique en santé au Canada en mettant en place un réseau et une infrastructure centralisés qui soutiennent l'innovation tout au long de son parcours -du laboratoire au chevet du patient -et en appuyant les réformes de l' enseignement.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.031 | 0.012 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".