Medical Education About End-of-Life Care in the Pediatric Setting: Principles, Challenges, and Opportunities
Bibliographic record
Abstract
OBJECTIVE: To identify the opportunities for and barriers to medical education about end-of-life (EOL) care in the pediatric setting. METHODS: A working group of pediatric specialists and ethicists was convened at the National Consensus Conference on Medical Education for Care Near the End-of-Life sponsored by the Open Society Institute's Project Death in America and the Robert Wood Johnson Foundation. The charge to the working group was to consider the unique aspects of death in childhood, identify critical educational issues and effective instructional strategies, and recommend institutional changes needed to facilitate teaching about EOL care for children. CONCLUSIONS: Although providing EOL care can be challenging, the cognitive and psychologic skills needed can be taught effectively through well-planned and focused learning experiences. The ultimate goals of such instruction are to provide more humane care to very sick children, enhance bereavement outcomes for their survivors, and develop more confident clinicians. Six specific principles regarding EOL care in the pediatric setting emerged as essential curricular elements that should be taught to all medical care providers to ensure competent patient-centered care. 1) Cognitively and developmentally appropriate communication is most effective. 2) Sharing information with patients helps avoid feelings of isolation and abandonment. 3) The needs of the patient are served when the ethical principles of self-determination and best interests are central to the decision-making process. 4) Minimization of physical and emotional pain and other symptoms requires prompt recognition, careful assessment, and comprehensive treatment. 5) Developing partnerships with families supports them in their caregiving efforts. 6) The personal and professional challenges faced by providers of EOL care deserve to be addressed. These principles actually transcend patient age and can be used to inform medical education about the care of any terminally ill patient. Similarly, these principles of effective communication, ethical decision-making, and attention to the quality of life of patients, families, and providers apply to the care of all children regardless of diagnosis and prognosis. With this in mind, teaching about EOL care does not require a new and separate curriculum, but rather taking better advantage of the many teachable moments provided by caring for a dying patient.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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".