Overview and factors associated with pregnancies and abortions occurring in sex workers in Benin
Bibliographic record
Abstract
Abstract INTRODUCTION The behavioural and structural factors related to sex work, place female sex workers (FSWs) at high risk of maternal mortality and morbidity (MMM), with a large portion due to unintended pregnancies and abortions. In the African context where MMM is high amongst the general population, understanding the frequency and determinants of pregnancy and abortion among FSWs is important in order to meet their sexual and reproductive health needs. METHODS Data from two Beninese cross-sectional surveys (2013 - 2016) were merged. We first performed exploratory univariate analyses to identify factors associated with pregnancy and abortion (p<0.20) using Generalized Estimating Equations with Poisson regression and robust variance. Multivariate analyses first included all the variables identified in the univariate models and backward selection (p≤0.05) was used to generate the final models. RESULTS Mean age was 31 years (N=866). The proportion of FSWs reporting at least one pregnancy during sex work was 16.4%, of whom 42.3% had more than one. Most pregnancies ended up with an abortion (67.6%). FSWs who lived with their boyfriend (adjusted proportion ratio (aPR) = 1.74, 95%CI: [1.1, 2.8]) and did not use condoms consistently with them (aPR=1.83, 95%CI: [1.1, 3.0]) had more pregnancies. CONCLUSION One FSW out of five had at least one pregnancy during her sex work practice. Most of those pregnancies, regardless of their origin, ended up with an abortion. Improving access to various forms of contraception and safe abortion is the key to reducing unwanted pregnancies and consequently, MMM among FSWs in Benin.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".