Scarce resources, public health and professional care: the COVID-19 pandemic exacerbating bioethical conflicts — findings from global qualitative expert interviews
Bibliographic record
Abstract
BACKGROUND: Since spring 2020, the SARS-CoV-2 virus has spread worldwide, causing dramatic global consequences in terms of medical, care, economic, cultural and bioethical dimensions. Although the resulting conflicts initially appeared to be quite similar in most countries, a closer look reveals a country-specific intensification and differentiation of issues. Our study focused on understanding and highlighting bioethical conflicts that were triggered, exposed or intensified by the COVID-19 pandemic in low and middle-income countries (LMICs) and high-income countries (HICs). METHODS: We conducted qualitative interviews with 39 ethics experts from 34 countries (Argentina, Australia, Austria, Brazil, Canada, Colombia, Denmark, Ecuador, Ethiopia, France, Germany, India, Italy, Israel, Japan, Kyrgyzstan, Mexico, Nigeria, Oman, Pakistan, Paraguay, Poland, Romania, Russia, Singapore, South Korea, Spain, Sweden, South Africa, Tunisia, Türkiye, United-Kingdom, United States of America, Zambia) from November 2020 to March 2021. We analysed the interviews using qualitative content analysis. RESULTS: The scale of the bioethical challenges between countries differed, as did coping strategies for meeting these challenges. Data analysis focused on: a) Resource scarcity in clinical contexts: Scarcity of medical resources led to the need to prioritize the care of some COVID-19 patients in clinical settings globally. Because this entails the postponement of treatment for other patients, the possibility of serious present or future harm to deprioritized patients was identified as a relevant issue. b) Health literacy: The pandemic demonstrated the significance of health literacy and its influence on the effective implementation of health measures. c) Inequality and vulnerable groups: The pandemic highlighted the context-sensitivity and intersectionality of the vulnerabilities of women and children in LMICs and the aged in HICs. d) Conflicts surrounding healthcare professionals: The COVID-19 outbreak underscored the tough working conditions for nurses and other health professionals, raising awareness of the need for reform. CONCLUSION: The pandemic exposed pre-existing structural problems in LMICs and HICs. Without neglecting individual contextual factors in the observed countries, we created a mosaic of different voices of experts in bioethics across the globe, drawing attention to the need for international solidarity in the context of a global crisis.
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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 | 0.045 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| 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".