Public perspectives on COVID-19 triage protocols for access to critical care in extreme pandemic context
Bibliographic record
Abstract
COVID-19 triage protocols are resource allocation processes to deal with the potential lack of resources in Intensive Care Units (ICU). They have given rise to numerous ethical issues and controversies. Among them is the fear that people will be denied access to ICU on the basis of judgments about their quality of life, social value, frailty or age. This online Democratic Deliberation (DD) with members of the public aimed to discover the necessary considerations and conditions that make triage protocols more acceptable to guide future decisions in terms of the values and criteria that must underpin triage protocols. We simultaneously conducted the online DD in Quebec and Ontario on May 28th and June 4th, 2022, among adults who do not work in the healthcare sector, recruited randomly among the members of the public registered on Leger Opinion poll website to favor sociodemographic diversity. Data was analyzed using thematic analysis. Among the participants who took part in the study, 27 participants were from Ontario and 20 from Quebec. Three main themes emerged: 1) Acceptance of the protocol and values, 2) Considerations to be integrated in triage protocols, 3) Conditions which may favor a greater public acceptance of these protocols. Participants supported the idea of prioritizing patients with the best prognosis of survival under extreme conditions. The maximization of benefits was the most predominant approach. Participants considered that triage protocols are necessary to reduce arbitrariness in decision making and to facilitate these tragic decisions by health professionals.
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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 | 0.040 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".