End- of- Life Care: Beneficence Undermines Patient’s Autonomy
Bibliographic record
Abstract
End-of-life care is a decision-making process in which health care providers, patients, and their families play a crucial role in easing the suffering of the patients and their families. Usually, end-of-life decision-making takes place in a critical situation of the patient; therefore, health care providers, particularly, physicians and nurses play a major role in making a decision for the patient’s life with regards to updated knowledge and practice. In this view, health care providers face many challenges in end-of-life decision-making due to controversy among equally unfavorable solutions; particularly between two ethical principles i.e., patient autonomy and beneficence. Health care providers often overweigh beneficence over autonomy regarding less suffering for the patient and his/her family. This approach of health care providers raises a question for undermining patients' autonomy and violating the basic ethical right of a patient. To overcome these kinds of ethical challenges, it is imperative to equip health care providers with updated knowledge of advance directives for patients. In addition, patients and their families should be well informed from the beginning to the end stage of the patient stay in the hospital. Besides, each hospital should have an ethical expert committee including nurses to analyze the entire situation and to make the decision in the best interest of the patient and his/her family.
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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 | 0.023 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 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".