Children as an afterthought during COVID-19: defining a child-inclusive ethical framework for pandemic policymaking
Bibliographic record
Abstract
BACKGROUND: Following the SARS pandemic, jurisdictions around the world began developing ethical resource allocation frameworks for future pandemics-one such framework was developed by Thompson and colleagues. While this framework offers a solid backbone upon which decision-makers can rest assured that their work is driven by rigorous ethical processes and principles, it fails to take into account the nuanced experiences and interests of children and youth (i.e., young people) in a pandemic context. The current COVID-19 pandemic offers an opportunity to re-examine this framework from young people's perspectives, informed by advances in childhood ethics and children's rights. MAIN BODY: In this paper, we revisit the Thompson et al. framework and propose adaptations to the ethical processes and values outlined therein. This work is informed by expertise in clinical ethics and literature related to impacts of COVID-19 and other pandemics on the health and well-being of children around the world, though with particular attention to Canada. During the processes of drafting this work, stakeholders were consulted-aligned with the approach used by Thompson and colleagues-to validate the interpretations provided. We also propose a new principle, namely practicability, to indicate the complex balance between what is possible and what is convenient that is required in ethically sound decisions in the context of services affecting young people. We outline and discuss the strengths and limitations of our work and indicate next steps for scholars in the areas of childhood studies and child health. CONCLUSION: Efforts to ensure frameworks are truly child-inclusive should be the status-quo, so pandemic impacts and policy implications can be considered in advance of emergency preparedness contexts.
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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 | 0.132 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.101 |
| Scholarly communication | 0.026 | 0.021 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 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".