Ethical Diversity and the Role of Conscience in Clinical Medicine
Bibliographic record
Abstract
In a climate of plurality about the concept of what is "good," one of the most daunting challenges facing contemporary medicine is the provision of medical care within the mosaic of ethical diversity. Juxtaposed with escalating scientific knowledge and clinical prowess has been the concomitant erosion of unity of thought in medical ethics. With innumerable technologies now available in the armamentarium of healthcare, combined with escalating realities of financial constraints, cultural differences, moral divergence, and ideological divides among stakeholders, medical professionals and their patients are increasingly faced with ethical quandaries when making medical decisions. Amidst the plurality of values, ethical collision arises when the values of individual health professionals are dissonant with the expressed requests of patients, the common practice amongst colleagues, or the directives from regulatory and political authorities. In addition, concern is increasing among some medical practitioners due to mounting attempts by certain groups to curtail freedom of independent conscience-by preventing medical professionals from doing what to them is apparently good, or by compelling practitioners to do what they, in conscience, deem to be evil. This paper and the case study presented will explore issues related to freedom of conscience and consider practical approaches to ethical collision in clinical medicine.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| 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".