Determinants of sexually transmitted infections among adolescent girls and young women in artisanal and small-scale mining communities of Uganda
Bibliographic record
Abstract
BACKGROUND: The artisanal and small-scale mining (ASM) sector has become an important employer in mineral rich countries of sub Saharan Africa where women constitute up to half of the labour force. However, gender and socio-economic marginalization negatively impact the sexual and reproductive health (SRH) of the adolescent girls and young women (AGYW) who work in the ASM sector. Despite the growing literature on adolescents' SRH, there is a paucity of literature on the SRH of this last mile population. This paper fills this gap in the literature by examining the prevalence and determinants of self reported sexually transmitted infection (STI) status among AGYW in the ASM gold mining sectors of Uganda. METHODS: The paper is based on 636 AGYW working in the mining sectors in Uganda who had ever had sex. Descriptive analysis involved frequency distributions and chi squared tests. Multivariable analysis involved fitting a binary logistic regression model to assess the determinants of self reported STI status of the AGYW. RESULTS: Almost half (47%) of the respondents had a self reported STI during the 12 months preceding the study. The odds of reporting an STI were higher among adult young women compared with minors (AOR = 3.35; 95% CI 1.82 - 6.16); AGYW with primary level of education compared to those with none (AOR = 2.89; 95% CI 1.24 - 6.75); who drank alcohol (AOR 1.59; 95% CI 1.06-2.39); and engaged in transactional sex (AOR 2.42; 95% CI 1.37 - 4.28). CONCLUSIONS: The results highlight the urgent need to respond to the high prevalence of self reported STIs among AGYW in ASM. The risk factors constitute multiple and intersecting vulnerabilities that require both preventive and curative interventions targeting female and male ASM workers and host communities, with emphasis on behavioral change and promotion of viable alternative sources of income. The ministries of Health, Gender, Labour and Social Development and key development partners should adopt a multi sectoral approach that effectively engages key stakeholders, including mining host communities, given the close interrelations between gender, health and economic aspects of the AGYW's lives.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".