Ontario's Digital Health Vision in the post-COVID-19 Pandemic Era: A Canadian Perspective
Bibliographic record
Abstract
The Canadian healthcare system has successfully enabled the average Canadian to live a longer life since the early 1980s. Yet, the prevalence of chronic diseases among Canadians is higher than ever, thereby increasing pressure on the healthcare system to develop a new vision based on the realities of the post-COVID-19 pandemic. The responsibility for Canada's healthcare is allocated amongst multiple actors and/or agencies, as the federal government and provinces/territories have significantly different responsibilities. Our study aims to discuss digital health strategies in Ontario, Canada. We examine best practices across the world and propose a digital health vision for Ontario and elsewhere. The lack of an integrated healthcare system often limits access to digital health tools, thus creating a fragmented digital health environment with organizational silos of health information. As a result, healthcare services may not use the advantages of digital health tools efficiently and effectively. We discuss some of the challenges of creating a digital health vision, such as financial feasibility, privacy, ease of use, and reaching vulnerable populations.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.026 | 0.018 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".