Additional file 1 of Global diversity of policy, coverage, and demand of COVID-19 vaccines: a descriptive study
Bibliographic record
Abstract
Additional file 1: Figure S1. The distribution of whether local residents need to pay for vaccine. Figure S2. Geographic distribution of overall technical platforms for vaccines. Figure S3. Proportion administered by vaccine technical platforms. Figure S4. Proportion administered by vaccine types. Figure S5. Date at which achieved one dose per 100 people in total population by country. Figure S6. Vaccine coverage over time stratified by income groups and role of vaccine seller/donor or recipient. Figure S7. Vaccine coverage stratified by SDI quintile and WHO region. Figure S8. The association between vaccine coverage with physician density and government health spending per capita. Figure S9. Corrections between vaccine coverage and country-level vaccine acceptance. Table S1. Authorization information of COVID-19 vaccines by technical platforms and country. Table S2. Target populations and contraindications recommended by regulatory agencies. Table S3. Policies on additional or booster dose of COVID-19 vaccine. Table S4. Country lists of selling/donating or receiving COVID-19 vaccines. Table S5. Categories and definitions of special population groups belonging to indication and contraindication lists. Table S6. Global, regional, and national target population (TP). Table S7. The list of variables for investigating associations with vaccine coverage. Table S8. Analysis of multicollinearity. Table S9. Global, regional, and national demand of vaccine dose.
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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 | 0.001 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.683 | 0.066 |
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".