Tracking progress along the WHO Neglected Tropical Diseases Road Map to 2030: A guide to the Gap Assessment Tool (GAT) and results from the 2023–2024 assessment
Bibliographic record
Abstract
The monitoring and evaluation (M&E) framework for the World Health Organization (WHO) Neglected Tropical Diseases (NTD) road map includes both quantitative and qualitative assessments of progress at key timepoints up to 2030. These assessments provide critical information to improve programmatic action and identify immediate research needs. Quantitative assessments process multi-sourced data on a predefined number of global indicators. Qualitative assessments are based on results obtained from implementing a Gap Assessment Tool (GAT). Informed by paradigm shifts detailed in the road map, a standardised methodology for the GAT has recently been developed. Methods include online public consultations and focus group discussions. GAT outcomes include a visualisation of progress in a 'heat map', a review of the current status of each NTD, and both disease-specific and cross-thematic recommendations for programmatic and research action. These outcomes provide valuable information not only for country NTD programmes, but also for global stakeholders, so that optimal efforts can be made to achieve established NTD road map goals, and ultimately, to reduce the burden of NTDs in at-risk populations. This manuscript provides a summary of the standardised GAT methodology and results from the implementation of the GAT in 2023-2024. Access is provided to the current status of each NTD with respect to four dimensions that were determined as priority in the 2019-2020 gap assessment (Diagnostics, Monitoring and Evaluation, Access & Logistics, and Advocacy & Funding). Results include a timely consensus on the critical and cross-cutting actions needed to improve respective programmatic impact towards eradication, elimination and control of NTDs globally and nationally.
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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 | 0.035 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.016 |
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".