Lessons from Yemen: Diphtheria and Polio Campaign in the Context of COVID-19
Bibliographic record
Abstract
Yemen conducted a diphtheria campaign in five governorates between 4 July and 19 July 2020, followed by a polio campaign in 13 governorates between 25 July and 17 August 2020. The study aimed at documenting lessons from conducting the campaigns within the context of COVID-19 pandemic in Yemen after their initial suspension in March 2020. The lessons could contribute to the evidence on the feasibility of maintaining and continuing vaccination campaigns in the context of COVID-19. The descriptive study relied on key informants and content analysis of planning and budgeting documents and daily monitoring reports as data sources. The COVID-19 precautions, including masks, gloves, hand sanitizers, and reduced crowding and social distancing, were applied during the campaigns. These measures minimized concerns over COVID-19, enabling the campaigns to go on, achieving 75% of its target for diphtheria and 96% of the polio campaign’s target. The provision of personal protective equipment increased the campaign’s perceived safety, leading to its smooth implementation. The measures constituted only about 4 percent of the entire cost of the campaign. The lessons learned will inform the planning and implementation of other upcoming vaccination-related activities in Yemen. This is also a good case study and experience for sharing with other countries.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".