No child is an island: ethical considerations in end-of-life care for children and their families
Bibliographic record
Abstract
PURPOSE OF REVIEW: Ethical challenges are commonly encountered when caring for children with life-threatening conditions. RECENT FINDINGS: Controversial end-of-life issues, such as physician-assisted death and medical futility, may also arise in children. The approach to these issues in children may be guided by the 'adult' medical literature; the age of the patient should not be a relevant factor in determining the morality of these acts. As such, the focus of this review is on ethical issues unique to children and adolescents by nature of their dependence on their parents. Appreciation that child well-being is best promoted when care aligns with parental beliefs, values and culture has given rise to the practice of family-centred care, which we prefer to call 'family-partnered' care. Occasionally, a family-partnered approach may challenge fundamental paediatric ethical principles, including best interests, developing autonomy, and the importance of honesty and truth-telling. SUMMARY: This article explores the challenges that may arise when there is disagreement between the child, the parents, and the healthcare providers about care at the end-of-life and provides suggestions to clinicians about how to help resolve these conflicts.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".