Bibliographic record
Abstract
Abstract This article showcases the relevance and complementarity of commonly used bioethics theories and frameworks for thinking about the challenges and moral tensions that (may) arise in efforts to move toward a more sustainable and ecological healthcare sector. It presents critical insights from deontology, consequentialism, virtue ethics, contractualism, modern casuistry, justice theories, and feminist approaches to bioethics, and points to important lessons from each of these for a green healthcare ethics in which acknowledging and dealing with potential, real, or apparent trade-offs is central. While ideal moral theories and frameworks such as deontology, consequentialism and virtue ethics can offer relevant normative principles to guide change at individual, organizational, and societal levels, other approaches such as contractualism and casuistry can offer practical and procedural guidance for addressing trade-off situations. In addition, justice theories and feminist approaches can offer normative grounds, respectively, for determining how to appropriately and equitably distributing the benefits, risks and burdens of specific initiatives or policies that are envisioned for transitioning to green healthcare sector, and for better understanding the role of complex human–human and human–environment relations and interdependencies in these discussions. These lessons provide foundations for the development of a comprehensive ethical framework, and we advocate for their future integration into a trade-off ethics .
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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 | 0.098 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.117 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".