Taking Risks to Protect Others—Pediatric Vaccination and Moral Responsibility
Bibliographic record
Abstract
The COVID-19 pandemic during 2020-2022 raised ethical questions concerning the balance between individual autonomy and the protection of the population, vulnerable individuals and the healthcare system. Pediatric COVID-19 vaccination differs from, for example, measles vaccination in that children were not as severely affected. The main question concerning pediatric vaccination has been whether the autonomy of parents outweighs the protection of the population. When children are seen as mature enough to be granted autonomy, questions arise about whether they have the right to decline vaccination and who should make the decision when parents disagree with each other and/or the child. In this paper, I argue that children should be encouraged to not only take responsibility for themselves, but for others. The discussion of pediatric vaccination in cases where this kind of risk-benefit ratio exists extends beyond the 2020-2022 pandemic. The pandemic entailed a question that is crucial for the future of public health as a global problem, that is, to what extent children should be seen as responsible decision-makers who are capable of contributing to its management and potential solution. I conclude that society should encourage children to cultivate such responsibility, conceived as a virtue, in the context of public health.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.050 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".