Ensuring no one is left behind: Urgent action required to address implementation challenges for NTD control and elimination
Bibliographic record
Abstract
Since the ambitious goals to eliminate and control neglected tropical diseases (NTDs) were launched, the crucial role of partnerships has been emphasized as a pathway to ensure success.With multiple drug donations, we have a good supply of medicines necessary to eliminate parasites from the body; with donors, key funding for research and implementation; with researchers, the capability to create the evidence base for recommendations; with nongovernmental development organizations (NGDOs), support for implementation; and with national programs, the willingness and impetus to accomplish these ambitious goals.A country's health system is almost always invoked as crucial to NTD implementation, with the claim that NTD programs contribute to the strengthening of health systems.Key partners, often missing at the table, are the endemic communities themselves.Yet we acknowledge that both communities and local health systems are the "backbone" of our programs.Some key implementation questions frequently arise in NTD programs: sustaining the motivation of community drug distributors, appropriateness of timing of mass drug administration (MDA) activities, the coverage-compliance gap [1], social mobilization, human resource constraints in low-and middle-income countries, inefficient or weak health systems, multiple reporting requirements and different funding cycles from donors, and many more (see Fig 1).While these issues continue to plague our NTD community, we have not committed the necessary levels of research funds, expertise, or priority to adequately answer these questions.Often termed "social science questions," these questions have been relegated to a category of research that is too difficult to conduct, too time-consuming, too costly, and seemingly less important than studies of drug efficacy or of the sensitivity of diagnostic tools.We argue that it is time to bring these issues to the table and give them the attention they merit.We propose five principle areas for consideration by the NTD community.First, let's rephrase these "social science" issues as implementation challenges that can be addressed by social science as well as epidemiological and other implementation research methods.Many disciplines such as anthropology, psychology, sociology, behavioral science, health economics, health services research, and other public health disciplines have the methods and tools to respond to these questions, which until now have been lumped together as "social science" questions.We need to acknowledge the strengths that each of these academic traditions can bring to our understanding of NTD elimination and control.In addition, we need to ensure that the program implementers rather than the researchers are in the driver's seat in the identification of the programmatic challenges and are valued
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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 | 0.054 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.021 | 0.025 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".