A case for vaccinating adolescent girls for protection against COVID-19 during pregnancy and childbirth in resource-limited settings
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has had severe implications worldwide, including increased adverse maternal and neonatal health outcomes. Vaccination is one way of protecting against these adverse health outcomes. However, in some low-resource settings, vaccine inequity has led to poor uptake of COVID-19 vaccination. There are very high rates of adolescent pregnancy in low-resource settings, which are likely to become even higher as we begin to see the full effects of COVID-19 lockdown measures, including school closures. Although the benefits of COVID-19 vaccination in adolescents are debated, we propose that adolescent girls should be prioritised in COVID vaccination roll out in low-resource settings. This is to provide protection from severe COVID-19 disease in pregnancy, preventing adverse maternal and neonatal health outcomes.
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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 | 0.016 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.034 | 0.005 |
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".