Access to Healthcare amongst Adolescent Girls and Young Women in Ugandan Artisanal and Small-Scale Mining Communities
Bibliographic record
Abstract
BACKGROUND: Artisanal small scale-mining (ASM) provides work to many adolescent girls and young women (AGYW) in Uganda. AGYW working in ASM face a unique set of health challenges such as exposure to mercury, HIV and sexually transmitted infections. AGYW in ASM are more vulnerable since they work and live in remote areas with limited health infrastructure which may limit their access to care. However, the AGYW's access to care, including SRH care, is not well understood or documented. This paper aims to explore the current healthcare access challenges and bottlenecks amongst Ugandan AGYW working in ASM communities. METHODS: The findings are part of a large study which was conducted in Busia, Mubende, and Namayingo communities, from three mining regions in Uganda. While the large study employed a mixed methods approach involving a survey with AGYW and qualitative data (in depth interviews, focus group discussions with adolescent boys, older women, and AGYW, key informant interviews with local leaders and policymakers). Data were analyzed deductively based on the Tanahashi Model on access to care. RESULTS: Overall, AGYW working in ASM communities expressed interest in using the health care facilities to treat their illnesses and for family planning services. However, with the exception of a few, they reported various challenges they face in accessing healthcare. All participants discussed challenges such as lack of personnel at facilities, lack of youth friendly health services, limited resources, distance to facilities, cost of care, and negative provider attitudes especially towards sexual and reproductive health care for unmarried AGYW. CONCLUSIONS: Despite some availability, accessibility, acceptability, and contact coverage, the gaps in AGYW's access to healthcare highlight a critical need to avail and strengthen health systems and especially sexual and reproductive health care in ASM communities in Uganda. This will contribute to achieving UHC and ultimately health equity. Mitigating the inequities in accessing care amongst ASM AGYW involves processes such as formalization, advocacy and rights-based accountability, and leveraging existing initiatives proposed in national Ugandan health policies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".