Freedom of Conscience in Health Care: Distinctions and Limits
Bibliographic record
Abstract
The widespread emergence of innumerable technologies within health care has complicated the choices facing caregivers and their patients. The escalation of knowledge and technical innovation has been accompanied by an erosion of moral and ethical consensus among health providers that is reflected in the abandonment of the Hippocratic Oath as the immutable bedrock of medical ethics. Ethical conflicts arise when the values of health professionals collide with the expressed wishes of patients or the dictates of regulatory bodies and administrators. Increasing attempts by groups outside of the medical profession to limit freedom of conscience for health providers has raised concern and consternation among some health professionals. The personal and professional impact of health professionals surrendering freedom of conscience and participating in actions they deem malevolent or unethical has not been adequately studied and may not be inconsequential when considering the recognized impact of other circumstances of coerced complicity. We argue that the distinction between the two ways that freedom of conscience is exercised (avoiding a perceived evil and seeking a perceived good) provides a rational basis for a principled limitation of this fundamental freedom.
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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 | 0.031 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.134 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.008 | 0.012 |
| 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".