Health Sector responses to the COVID-19 pandemic in Ontario, Canada – January to May 2020
Bibliographic record
Abstract
The first positive case of COVID-19 in Canada was reported on January 25, 2020, in the city of Toronto, Ontario. Over the following four months, the number of individuals diagnosed with COVID-19 in Ontario grew to 28,263 cases. A state of emergency was announced by the Premier of Ontario on March 17, 2020, and the provincial health care system prepared for a predicted surge of COVID-19 patients requiring hospitalization. The Chief Medical Officer of Health and the Minister of Health guided the changes in the system in response to the evolving needs and science related to COVID-19. The pandemic required a rapid, concerted, and coordinated effort from all sectors of the system to optimize and maximize the capacity of the health system. The response to the pandemic in Ontario was complex with some sectors experiencing multiple outbreaks of COVID-19 (i.e. long-term care homes and hospitals). Notably, numerous sectors shifted to virtual delivery of care. By the end of May 2020, it was announced that hospitals would gradually resume postponed or cancelled services. This paper explores the impact of the COVID-19 pandemic on multiple health system sectors (i.e., public health, primary care, long-term care, emergency medical services, and hospitals) in Ontario from January to May 2020. Given the scope of the sectors contributing to the health system in Ontario, this analysis of a regional response to COVID-19 provides insight on how to improve responses and better prepare for future health emergencies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".