Exploring the impact of mobile and migrant populations on mass drug administration coverage and effectiveness in Africa: A scoping review protocol
Bibliographic record
Abstract
BACKGROUND: Neglected tropical diseases (NTDs) affect populations in tropical regions, particularly low- and middle-income countries with limited economic and health resources. Mass drug administration (MDA) is a strategy for controlling and eliminating NTDs by treating entire at-risk populations to reduce parasite loads, interrupt transmission, and prevent reinfection. It is cost-effective, and promotes equity by reaching underserved communities. MDA is a critical approach to controlling and eliminating NTDs. Mobile populations in Africa such as nomadic groups and internally displaced persons, may lack access to MDA, which poses challenges to NTD elimination. This study aims to explore the influence of population mobility on the implementation, effectiveness, and sustainability of MDA in Africa. MATERIALS AND METHODS: This scoping review adheres to the PRISMA extension for scoping reviews and Joanna Briggs Institute (JBI) methodology. PCC (Population, Concept, Context) serves as the foundation for the study. Relevant papers published after 2000 will be identified through a comprehensive search of Medline Ovid, Embase, Web of Science, and gray literature. Studies addressing challenges to MDA in Africa's and related to mobile populations will be included. An Excel spreadsheet modified from the JBI will be used for data extraction and analysis. CONCLUSION: The results of this review will shed light on how MDA coverage is affected by the phenomenon of mobile and migrant populations and what effective approaches, if any, have been used to address this problem and improve overall population access to MDA.
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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 | 0.092 | 0.084 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.053 | 0.009 |
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".