COVID-19 critical care triage across Canada: a narrative synthesis and ethical analysis of early provincial triage protocols
Bibliographic record
Abstract
PURPOSE: The COVID-19 pandemic created conditions of scarcity that led many provinces within Canada to develop triage protocols for critical care resources. In this study, we sought to undertake a narrative synthesis and ethical analysis of early provincial pandemic triage protocols. METHODS: We collected provincial triage protocols through personal correspondence with academic and political stakeholders between June and August 2020. Protocol data were extracted independently by two researchers and compared for accuracy and agreement. We separated data into three categories for comparative content analysis: protocol development, ethical framework, and protocol content. Our ethical analysis was informed by a procedural justice framework. RESULTS: We obtained a total of eight provincial triage protocols. Protocols were similar in content, although age, physiologic scores, and functional status were variably incorporated. Most protocols were developed through a multidisciplinary, expert-driven, consensus process, and many were informed by influenza pandemic guidelines previously developed in Ontario. All protocols employed tiered morality-focused exclusion criteria to determine scarce resource allocation at the level of regional health care systems. None included a public engagement phase, although targeted consultation with public advocacy groups and relevant stakeholders was undertaken in select provinces. Most protocols were not publicly available in 2020. CONCLUSIONS: Early provincial COVID-19 triage protocols were developed by dedicated expert committees under challenging circumstances. Nonetheless, few were publicly available, and public consultation was limited. No protocols were ever implemented, including during periods of extreme critical care surge. A national approach to pandemic triage that incorporates additional aspects of procedural justice should be considered in preparation for future pandemics.
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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 | 0.222 | 0.345 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".