Bibliographic record
Abstract
Objective Paediatric ethics has focused predominantly on debating treatment decision-making authority between parents and physicians. These debates have been important in identifying the issues surrounding “who should decide?” and have given rise to decisional models that are congruent with the social roles attributed to parents and physicians in different countries. A commonly underacknowledged issue is the significance that should be accorded to the voice of the child undergoing medical treatment. Children’s treatment wishes are generally disregarded until they can demonstrate adult-like decision-making capacities. Methods and Results This paper highlights three major problems associated with typical practices regarding children’s participation in treatment decision-making: (1) children’s decisional capacities are commonly underestimated (ie, young adolescents demonstrate decision-making judgement resembling that of adults); (2) children (including very young children) have moral viewpoints that are unique and quite distinctive from those of adults that are morally meaningful to them, yet typically disregarded by adults and (3) adults deciding on behalf of children’s best interests usually have their own adult interests that can conflict with those of the children. Conclusion In an attempt to overcome these barriers to “hearing” the voices of children in treatment decisions, I will argue for a stronger recognition of children’s assent to treatment.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".