Water, sanitation, and hygiene challenges in last-mile artisanal mining communities in Ghana and Uganda
Bibliographic record
Abstract
Introduction: Access to water, sanitation, and hygiene (WASH) is critical for public health but remains inadequate in marginalized areas, particularly in sub-Saharan Africa's artisanal and small-scale mining (ASM) communities. Adolescent girls and young women (AGYW) in these settings face unique challenges that impact their health and wellbeing. Objective: This study aimed to assess WASH access among adolescent girls and young women (aged 10-24) in last-mile ASM communities in Ghana and Uganda, identifying disparities and factors influencing access. Methods: A cross-sectional, mixed-methods design was employed between March and May 2022 in selected ASM communities in Ghana and Uganda. The quantitative component included a sample of 1618 AGYW (808 in Ghana, 810 in Uganda) recruited through random household selection. Data were collected using interviewer-administered questionnaires adapted from validated sources, covering socio-demographics, water sources, sanitation, and hygiene practices. Descriptive statistics, chi-square tests, and logistic regression were conducted, stratified by country, to examine associations between WASH access and sociodemographic factors. For the qualitative component, AGYW, community leaders, district officers, policymakers, and global experts were purposively selected. Data was collected through focus group discussions and in-depth/key informant interviews conducted in local languages. Thematic analysis was performed using NVivo 12, with illustrative participant quotes. Results: Quantitative findings showed that 86.2 % reported access to improved water sources, but only 10.1 % had access to improved toilet facilities. In Ghana, 83 % lacked any toilet facility; in Uganda, 65 % used unimproved latrines. Water access was associated with religion and education in Ghana, and toilet access was linked to residence and wealth in both countries. Qualitative findings revealed concerns about water quality, reliability, distance to water points, and major sanitation challenges, especially for women and girls. Cultural norms and mining-related environmental impacts further exacerbated WASH vulnerabilities. Conclusion: Significant disparities in WASH persist in ASM communities, particularly for sanitation. Context-specific, community-engaged interventions are urgently needed to address these gaps and promote health equity for AGYW in rural mining settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".