COVID-19 pandemic: the impact on Canada’s intensive care units
Bibliographic record
Abstract
The COVID-19 pandemic has exposed the precarious demand-capacity balance in Canadian hospitals, including critical care where there is an urgent need for trained health care professionals to dramatically increase ICU capacity. The impact of the pandemic on ICUs varied significantly across the country with provinces that implemented public health measures later and relaxed them sooner being impacted more severely. Pediatric ICUs routinely admitted adult patients. Non-ICU areas were converted to ICUs and staff were redeployed from other essential service areas. Faced with a lack of critical care capacity, triage plans for ICU admission were developed and nearly implemented in some provinces. Twenty eight percent of patients in Canadian ICUs who required mechanical ventilation died. Surviving patients have required prolonged ICU admission, hospitalization and extensive ongoing rehabilitation. Family members of patients were not permitted to visit, resulting in additional psychological stresses to patients, families, and healthcare teams. ICU professionals also experienced extreme psychological stresses from caring for such large numbers of critically ill patients, often in sub-standard conditions. This resulted in large numbers of health workers leaving their professions. This pandemic is not yet over, and it is likely that new pandemics will follow. A review and recommendations for the future are provided.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".