A case study of Gavi'S human papillomavirus vaccine support programme
Bibliographic record
Abstract
Human papillomavirus (HPV), a sexually transmitted DNA virus that can lead to cervical cancer, is the most common cancer among women in developing regions. More than 270,000 women die per year from cervical cancer globally, and 85% of those deaths occur in developing countries. In the past, many low- and middle-income countries (LMICs) have been unable to afford the implementation of HPV vaccination programmes, resulting in high cervical cancer mortality rates. Gavi, an organisation created to improve worldwide access to vaccines, undertook an initiative that had the goal of decreasing the price of an HPV vaccine to under $5 and increasing access for adolescent girl populations in LMICs. This was done through market shaping, co-financing and implementation support. This case study will present and evaluate Gavi's intervention by assessing targets, investigating cost-effectiveness and identifying strategic challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".