Ethical implications for children’s exclusion in the initial COVID-19 vaccination in Ghana
Bibliographic record
Abstract
Bioethics provides various models of fair allocation of scarce health resources like COVID-19 vaccines. Even though these models are grounded in some ethical principles like justice and beneficence, there were severe inequalities in global access to COVID-19 vaccines. In Ghana, about 21.5 million COVID-19-doses have been administered but comprise mainly members of the adult population. As a result, ethical issues related to vaccinating children have been largely ignored in the country. This paper explores some of the ethical implications related to children's exclusion in the initial COVID-19 vaccination programs in Ghana. It provides a general overview of the COVID-19 pandemic in Ghana and how it related to children and discusses the risks to which Ghanaian children were exposed by delaying their COVID-19 vaccination. A guide to facilitating the full rollout of COVID-19 vaccination in Ghana for children has been proposed that indicates that a fair vaccine distribution for children should prioritize children on admission at health facilities, those diagnosed with severe underlying health conditions, and children who could play an instrumental role in promoting vaccine uptake. It concludes that children must not be placed at the peripheries of the COVID-19 vaccination program in Ghana.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| 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".