The big shift in Canadian healthcare: connected, integrated and community-based
Bibliographic record
Abstract
The Canadian healthcare system (medicare) was established by legislation passed in 1984 and has become a hallmark of our society. Designed on the principle that healthcare is a social good and that all residents should have equal access to the appropriate medical care, the Canadian healthcare system still garners overwhelming public support. However, emerging challenges threaten to destabilize a healthcare system cherished by many. Over the long term, the existing healthcare delivery model will no longer be sufficient to meet the demands of the Canadian public. In order to provide the best possible care in the most affordable and efficient manner, Canada’s provinces and territories are working to modernize the way they provide healthcare products and services, an effort fueled by the ubiquitous availability of technology. In this report we investigate the two interdependent thrusts that underpin this radical transformation: Decentralization: Moving care outside of provider settings and into the home and community Connectivity: Open data sharing and communication across users and healthcare providers Together, decentralization and connectivity have significant potential to address some of the current healthcare system’s greatest challenges and, importantly, may result in better health outcomes for citizens, while reducing the financial burden on the public purse. Successful implementation will require the combined and unified efforts of decision makers, healthcare professionals, healthcare institutions (hospitals), community-based facilities and patients. This report is part of the Connected World series and will investigate the many aspects of community-based and connected care. As Part 1, it is intended to provide background on the factors shaping the transformation of healthcare. An upcoming report (Part 2) will delve into the technologies required to successfully move care into the community and to connect all system players. Ontario entrepreneurs and researchers are hard at work developing new innovations that will fundamentally transform the existing healthcare delivery model. The follow-up report (Part 2) will highlight a selection of innovators and showcase the progress that Ontario is making.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".