The Drivers of Low Vaccination Utilization in Niger
Bibliographic record
Abstract
Vaccination adherence involves the expected benefit of a vaccine and the perceived risk of the disease. To develop an evidence-based communication strategy aimed at improving vaccination coverage in Niger, we conducted a mixed socio-anthropological study of the perceptions among the population on the benefit and the risk of the childhood (Expanded Program on Immunization) and the COVID-19 vaccines. Our results show that negative rumors are not a significant driver of vaccine refusal. The insufficient level of fully vaccinated, compared with partially vaccinated, children might be explained by misunderstandings around the side effects of vaccines and the necessity for full vaccination. Approximately one-fourth of the population is vaccinated against COVID-19, whereas 73.3% think that vaccines against the disease are a "good thing," and 83% of those who have heard messages promoting the vaccination approve of them. This apparent contradiction is explained by a low perception of the risks of COVID-19. More than half of the population surveyed believe that the disease is not present in the country. A large majority believe that only ill people can transmit the disease, whereas only 12.8% think they know anybody who has ever been sick with COVID-19. Three-fourths of the interviewees have seen images from around the world of persons sick or deceased from COVID-19; the same proportion has not seen any such images of affected patients in Niger. Communication to improve COVID-19 uptake should focus on the reality of the disease presence and its transmission and not on rumors surrounding the vaccines.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".