Keeping communities at the centre of efforts to eliminate lymphatic filariasis: learning from the past to reach a future free of lymphatic filariasis
Bibliographic record
Abstract
Since the launch of the Global Programme to Eliminate Lymphatic Filariasis (GPELF) in 2000, more than 910 million people have received preventive chemotherapy for lymphatic filariasis (LF) and many thousands have received care for chronic manifestations of the disease. To achieve this, millions of community drug distributors (CDDs), community members and health personnel have worked together each year to ensure that at-risk communities receive preventive chemotherapy through mass drug administration (MDA). The successes of 20 y of partnership with communities is celebrated, including the application of community-directed treatment, the use of CDDs and integration with other platforms to improve community access to healthcare. Important challenges facing the GPELF moving forward towards 2030 relate to global demographic, financing and programmatic changes. New innovations in research and practice present opportunities to encourage further community partnership to achieve the elimination of LF as a public health problem. We stress the critical need for community ownership in the current Covid-19 pandemic, to counter concerns in relaunching MDA programmes for LF.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 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".