Local tips, global impact: community-driven measures as avenues of promoting inclusion in the control of neglected tropical diseases: a case study in Kenya
Bibliographic record
Abstract
BACKGROUND: Neglected tropical diseases (NTDs) affect poor populations with little or no 'political voice' to influence control activities. While most NTDs have interventions that work, the biggest challenge remains in delivering targeted interventions to affected populations residing in areas experiencing weak health systems. Despite the upward development trends in most countries of sub-Saharan Africa (SSA), the healthcare worker to population ratio remains exceptionally low, with some areas not served at all; thus, there is a need to involve other personnel for school and community-based healthcare approaches. Nonetheless, the current community-based programs suffer from inconsistent community participation due to a lack of coordinated response, and an expanded intervention agenda that lacks context-specific solutions applicable to rural, urban, and marginalized areas. METHODS: This research investigated the capacity of local communities to address the burden of NTDs. Informed by the social theory of human capability, the research collected primary qualitative data by conducting key informant interviews and focus group discussions of people infected or affected by NTDs. The interview data were collected and transcribed verbatim for thematic analysis using Nvivo version 12. RESULTS: Our findings reveal, first, a need for intersectoral collaboration between governments and affected populations for inclusive and sustainable NTD solutions. Second, a 'bottom-up' approach that enhances capacity building, sensitization, and behaviour change for improved uptake of NTD interventions. Third, the enforcement of Public Health Legislative Acts that mandates the reporting and treatment of NTDs such as leprosy. Fourth, the establishment of support groups and counseling services to assist persons suffering from debilitating and permanent effects of NTDs. CONCLUSIONS: Our research demonstrates the importance of human agency in encouraging new forms of participation leading to the co-production of inclusive and sustainable solutions against NTDs.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".