The Impact of Neglected Tropical Diseases (NTDs) on Women’s Health and Wellbeing in Sub-Saharan Africa (SSA): A Case Study of Kenya
Bibliographic record
Abstract
Neglected Tropical Diseases (NTDs) trap individuals in a cycle of poverty through their devastating effects on health, wellbeing and social–economic capabilities that extend to other axes of inequity such as gender and/or ethnicity. Despite NTDs being regarded as equity tracers, little attention has been paid toward gender dynamics and relationships for gender-equitable access to NTD programs in sub-Saharan Africa (SSA). This paper examines the impact of NTDs on women’s health and wellbeing in SSA using Kenya as a case study. This research is part of a larger research program designed to examine the impact of NTDs on the health and wellbeing of populations in Kenya. Thematic analysis of key informants’ interviews (n = 21) and focus groups (n = 5) reveals first that NTDs disproportionately affect women and girls due to their assigned gender roles and responsibilities. Second, women face financial and time constraints when accessing health care due to diminished economic power and autonomy. Third, women suffer more from the related social consequences of NTDs (that is, stigma, discrimination and/or abandonment), which affects their health-seeking behavior. As such, we strongly suggest a gender lens when addressing NTD specific exposure, socio-economic inequities, and other gender dynamics that may hinder the successful delivery of NTD programs at the local and national levels.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".