Vaccine inequity and hesitancy persist—we must tackle both
Bibliographic record
Abstract
Since the start of the covid-19 vaccination rollout, repeated concerns have been raised about global vaccine inequity.1 -5 In an April 2022 commentary in BMJ Global Health, we called specific attention to the importance of minimising vaccine wastage as a strategy for reducing vaccine inequities.6 While much of the world now has access to vaccines, both the United Nations' Data Futures Platform and the World Health Organization maintain that regional access to vaccines and their global uptake remain issues.7 8 Covid-19 persists as a threat to public health despite the desire of many governments to move on from it.In fact, WHO still considers the world to be in the emergency phase of the pandemic.Unfortunately, inequitable access to vaccines remains a challenge, especially in low and middle income countries.9 Just 24.6% of people in low income countries have received at least one vaccine dose.10 Provenance and peer review: Commissioned, not externally peer reviewed.1 Tsundue T, Namdon T, Tsewang T, etal.First and second doses of Covishield vaccine provided high level of protection against SARS-CoV-2 infection in highly transmissible settings: results from a prospective cohort of participants residing in congregate facilities in India.
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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 | 0.033 | 0.160 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.074 | 0.055 |
| Insufficient payload (model declined to judge) | 0.029 | 0.010 |
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".