Implementation of school-based mass drug administration of praziquantel in Nigeria: barriers, facilitators and opportunities for improvement
Bibliographic record
Abstract
Objective The World Health Organization (WHO) recommends annual preventive chemotherapy with a single dose of praziquantel at ≥ 75% treatment coverage, but as of 2021, the national coverage rate in Nigeria among children falls below 10%. This qualitative study sought to explore the barriers and facilitators to implementing large-scale praziquantel mass drug administration (MDA) programs for school-aged children in Nigeria to delineate tools and strategies that could improve the scaling-up and effectiveness of school-based praziquantel MDA programs. Methods An exploratory qualitative study was conducted with stakeholders with experience in MDAs involving praziquantel or related preventive chemotherapy drugs in Nigeria. Through snowball sampling, 30 stakeholders with experience in praziquantel school-based MDA in Nigeria were interviewed. An inductive approach was used to generate broad themes based on the barriers and facilitators identified by the key informants Results A total of 45 barriers and 36 facilitators were identified and grouped inductively into eight themes: funding and resources, design and composition of praziquantel tablets, knowledge and awareness mobilization, government, nongovernmental organization, and school engagement, data management, logistics, training, and security. Conclusion This qualitative study reveals a wide range of barriers and facilitators in the MDA of praziquantel in Nigeria and uncovered critical points along the implementation pathway based on the locus of the barriers and facilitators identified. Collaboration with national, international, and non-profit organizations, and drug education through promotional materials, were the most frequently mentioned facilitators of the MDA program. In addition, insufficient program funding, and the complexity of the supply chain were the most cited barriers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".