Community perceptions and acceptability of mass drug administration for the control of neglected tropical diseases in Asia-Pacific countries: A systematic scoping review of qualitative research
Bibliographic record
Abstract
BACKGROUND: Preventative chemotherapy and mass drug administration have been identified as effective strategies for the prevention, treatment, control and elimination of several NTDs in the Asia-Pacific region. Qualitative research can provide in-depth insight into the social dynamics and processes underlying effective implementation of and adherence to mass drug administration programs. This scoping review examines published qualitative literature to examine factors influencing community perceptions and acceptability of mass drug administration approaches to control NTDs in the Asia-Pacific region. METHODOLOGY: Twenty-four peer reviewed published papers reporting qualitative data from community members and stakeholders engaged in the implementation of mass drug administration programs were identified as eligible for inclusion. FINDINGS: This systematic scoping review presents available data from studies focussing on lymphatic filariasis, soil-transmitted helminths and scabies in eight national settings (India, Indonesia, Philippines, Bangladesh, Laos, American Samoa, Papua New Guinea, Fiji). The review highlights the profoundly social nature of individual, interpersonal and institutional influences on community perceptions of willingness to participate in mass drug administration programs for control of neglected tropical diseases (NTD). Future NTD research and control efforts would benefit from a stronger qualitative social science lens to mass drug administration implementation, a commitment to understanding and addressing the social and structural determinants of NTDs and NTD control in complex settings, and efforts to engage local communities as equal partners and experts in the co-design of mass drug administration and other efforts to prevent, treat, control and eliminate NTDs. CONCLUSION: For many countries in the Asia-Pacific region, the "low hanging fruit has been picked" in terms of where mass drug administration has worked and transmission has been stopped. The settings that remain-such as remote areas of Fiji and Papua New Guinea, or large, highly populated, multi-cultural urban settings in India and Indonesia-present huge challenges going forward.
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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 | 0.094 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".