The impact of Neglected Tropical Diseases (NTDs) on health and wellbeing in sub-Saharan Africa (SSA): A case study of Kenya
Bibliographic record
Abstract
Neglected Tropical Diseases (NTDs) remain endemic to many regions of sub-Saharan Africa (SSA) left behind by socioeconomic progress. As such, these diseases are markers of extreme poverty and inequity that are propagated by the political, economic, social, and cultural systems that affect health and wellbeing. As countries embrace and work towards achieving the Sustainable Development Goals (SDGs), the needs of such vulnerable populations need to be addressed in local and global arenas. The research uses primary qualitative data collected from five NTD endemic counties of Kenya: interviews key informants (n = 21) involved in NTD implementation programs and focus groups (n = 5) of affected individuals. Informed by theories of political ecology of health, the research focuses on post-devolution Kenya and identifies the political, economic, social, and cultural factors that propagate NTDs and their effects on health and wellbeing. Our findings indicate that structural factors such as competing political interests, health worker strikes, inadequate budgetary allocations, economic opportunity, marginalization, illiteracy, entrenched cultural norms and practices, poor access to water, sanitation and housing, all serve to propagate NTD transmission and subsequently affect the health and wellbeing of populations. As such, we recommend that post-devolution Kenya ensures local political, economic and socio-cultural structures are equitable, sensitive and responsive to the needs of all people. We also propose poverty alleviation through capacity building and empowerment as a means of tackling NTDs for sustained economic opportunity and productivity at the local and national level.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".