High rates of Unintended Pregnancies among Young Women Sex Workers in Conflict-affected Northern Uganda: The Social Contexts of Brothels/Lodges and Substance Use
Bibliographic record
Abstract
This study aimed to examine the correlates of unintended pregnancies among young women sex workers in conflict-affected northern Uganda. Data were drawn from the Gulu Sexual Health Study, a cross-sectional study of young women engaged in sex work. Bivariable and multivariable logistic regression was used to examine the correlates of ever having an unintended pregnancy. Among 400 sex workers (median age=20 years; IQR 19-25), 175 (43.8%) reported at least one unintended pregnancy. In multivariable analysis, primarily servicing clients in lodges/brothels [Adjusted Odds Ratio (AOR= 2.24; 95% Confidence Interval: 1.03-4.84)], hormonal contraceptive usage [AOR=1.68; 95%CI 1.11-2.59] and drug/alcohol use while working [AOR= 1.64; 95%CI 1.04-2.60] were positively correlated with previous unintended pregnancy. Given that unintended pregnancy is an indicator of unmet reproductive health need, these findings highlight a need for improved access to integrated reproductive health and HIV services, catered to sex workers' needs. Sex work-led strategies (e.g., peer outreach) should be considered, alongside structural strategies and education targeting brothel/lodge owners and managers.
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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 | 0.003 |
| 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 | 0.001 |
| 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".