Children with cancer in India: An ethical framework for practice
Bibliographic record
Abstract
BACKGROUND: Childhood cancer has been ranked the most common cause of death due to non-communicable disease among 5- to 14-year-old children in India. Ethical concerns have been identified in the care of children with cancer in India, yet there is a paucity of ethical standards for clinical practice to help address these concerns. For example, emerging research has demonstrated that many children are distressed when they are impeded from participating in discussions and decisions regarding their cancer care. Therefore, we sought to create an ethical framework to guide practice with this population. METHODS: We developed this ethical framework by conducting (a) a normative analysis of relevant documents that articulate norms for healthcare providers working with children in India and (b) stakeholder consultations with childhood cancer survivors, parents, and clinicians. RESULTS: The ethical framework is structured according to twelve key ethical principles and corresponding challenges or implications for clinical practice. We discuss how this ethical framework can help address three leading ethical concerns that we have identified within the care of children with cancer in India: (a) communication problems; (b) inadequate care of symptoms or promotion of comfort; and (c) injustices or inequities related to limited financial means or poverty. CONCLUSION: Ethical concerns that have been related to the care of children with cancer in India can be prevented or at least mitigated through the integration of this ethical framework in everyday clinical practice.
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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 | 0.086 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.079 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.007 | 0.016 |
| 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".