Denial as an ethical problem: the example of ICU triage in the context of the COVID-19 pandemic
Bibliographic record
Abstract
Objectives: The overall goal of this article is to show that denial is one of the greatest obstacles to good practical judgment and is therefore a major problem in clinical ethics by examining its cognitive structure and the challenges it poses for clinical ethics consultation and intervention. In addition to clinical examples, excerpts of verbatim from citizen forums on triage protocols will be used to illustrate the manifestations of denial in citizens when faced with difficult choices. Case presentation: The initial waves of the pandemic and the alarming resurgence of cases with the emergence of highly transmissible variants have created increased pressure on many healthcare systems around the world. These critical situations have activated the potential for health authorities in different countries to use triage protocols to manage access to critical care. In several cases, public opinion was alerted, creating a climate of concern and even suspicion among the general population. These debates have highlighted both the importance and the difficulty of basing triage choices and the allocation of scarce resources on an ethical or moral reasoning that commands strong support. The obstacles to this consensus are numerous. There is, of course, the diversity of beliefs and values, but also a mechanism that has been very little documented in clinical ethics: denial. Conclusions: Denial poses major problems for providers and professionals in healthcare settings. In the face of maladaptive behaviors such as denial, psychotherapy uses techniques that act on both the cognitive and affective levels. Many of these techniques require long-term work that can only be accomplished in the context of professionally supervised therapy, but some tips can be identified for mediation and the work of the clinical ethicist.
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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 | 0.020 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.025 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".