Exploring Gender Dimensions of Treatment Programmes for Neglected Tropical Diseases in Uganda
Bibliographic record
Abstract
BACKGROUND: Gender remains a recognized but relatively unexamined aspect of the potential challenges for treatment programmes for Neglected Tropical Diseases (NTDs). We sought to explore the role of gender in access to treatment in the Uganda National Neglected Tropical Disease Control Programme. METHODOLOGY/PRINCIPAL FINDINGS: Quantitative and qualitative data was collected in eight villages in Buyende and Kamuli districts, Eastern Uganda. Quantitative data on the number of persons treated by age and gender was identified from treatment registers in each village. Qualitative data was collected through semi-structured interviews with sub-county supervisors, participant observation and from focus group discussions with community leaders, community medicine distributors (CMDs), men, women who were pregnant or breastfeeding at the time of mass-treatment, and adolescent males and females. Findings include the following: (i) treatment registers are often incomplete making it difficult to obtain accurate estimates of the number of persons treated; (ii) males face more barriers to accessing treatment than women due to occupational roles which keep them away from households or villages for long periods, and males may be more distrustful of treatment; (iii) CMDs may be unaware of which medicines are safe for pregnant and breastfeeding women, resulting in women missing beneficial treatments. CONCLUSIONS/SIGNIFICANCE: Findings highlight the need to improve community-level training in drug distribution which should include gender-specific issues and guidelines for treating pregnant and breastfeeding women. Accurate age and sex disaggregated measures of the number of community members who swallow the medicines are also needed to ensure proper monitoring and evaluation of treatment programmes.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".