Ethical uncertainty and COVID-19: exploring the lived experiences of senior physicians at a major medical centre
Bibliographic record
Abstract
Given the wide-reaching and detrimental impact of COVID-19, its strain on healthcare resources, and the urgent need for-sometimes forced-public health interventions, thorough examination of the ethical issues brought to light by the pandemic is especially warranted. This paper aims to identify some of the complex moral dilemmas faced by senior physicians at a major medical centre in Saudi Arabia, in an effort to gain a better understanding of how they navigated ethical uncertainty during a time of crisis. This qualitative study uses a semistructured interview approach and reports the findings of 16 interviews. The study finds that participants were motivated by a profession-based moral obligation to provide care during the toughest and most uncertain times of the pandemic. Although participants described significant moral dilemmas during their practice, very few identified challenges as ethical in nature, and in turn, none sought formal ethics support. Rather, participants took on the burden of resolving ethical challenges themselves-whenever possible-rationalising oft fraught decisions by likening their experiences to wartime action or by minimising attention to the moral. In capturing these accounts, this paper ultimately contemplates what moral lessons can, and must be, learnt from this experience.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.021 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| 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".