Bioethics in the Public and Policy Spaces: Lessons from the Covid Years
Bibliographic record
Abstract
The Covid-19 pandemic presented numerous ethical challenges, highlighting the critical role of bioethicists in public spaces and policymaking. Bioethicists acted as guardians against systemic injustices, critics of health policy decisions, and contributors to public debate. This text draws on our experiences as North American academic bioethicists to explore the different roles that bioethicists took during the pandemic, notably through media engagement, participation in policy-making, and in research and education. The pandemic underscored the importance of bioethics in the healthcare system and in research governance, the need for interdisciplinary collaboration, the importance of applying various ethics frameworks, and the need for effective communication to ensure practical ethical decision-making. It also demonstrated the distinct yet complementary roles of academic and professional bioethicists, with the former often serving as visible public critics, due to their academic liberty and independence, while the latter worked within their institutions to support clinicians and decision-makers, and to effect policy change. But these roles could also lead to tensions between academic and professional bioethicists, due to their different mandates, and both also experienced frustrations with the continued lack of understanding by some professionals and policy-makers regarding the pertinence and utility of bioethics to support ethically-informed decision-making. Ultimately, the pandemic was a pivotal time for bioethicists to influence public debate and policy, showcasing the field’s relevance and adaptability in addressing complex ethical issues.
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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 | 0.053 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.035 | 0.065 |
| Scholarly communication | 0.032 | 0.027 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.020 | 0.034 |
| Insufficient payload (model declined to judge) | 0.006 | 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".