Children and COVID-19 Vaccine: A Public Health Ethics Perspective
Bibliographic record
Abstract
As COVID-19 cases were in rise all over the world and the World Health Organization declared a pandemic, there was an increasing focus on availability of new vaccines and drugs against the virus. Meanwhile, we already have several vaccines in COVID-19 vaccination programmes across the globe. During the process of development and clinical trials of the vaccines, several questions were popped up by multiple stakeholders about child vaccination against COVID-19. Most of the queries focused on safety of COVID-19 vaccines, the clinical trial process, priority criteria of getting a vaccine, why and why not children be included in the vaccination programme. In adult population of the country, COVID-19 vaccination programme is being carried out in an unequalled state; the focus is now on paeditric population, as some countries have already started to vaccinate children. At the time of writing this paper when Government of Bangladesh has not yet decided to vaccinate children in the country but initiatives has been taken by health department for above 12 years children vaccination. However, this paper aims to discuss potential ethical dilemmas related to COVID-19 vaccination in children especially in low-resource settings and dig into effective strategies to implement COVID-19 vaccination programme properly in the field of public health. Bangladesh Med J. 2021 Sept; 50(3): 44-48
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.029 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.017 | 0.020 |
| 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".