The preventable burden of mortality from unsafe abortion among female sex workers: a Community Knowledge Approach survey among peer networks in eight countries
Bibliographic record
Abstract
Previous studies have found high levels of unintended pregnancy among female sex workers (FSW), but less attention has been paid to their abortion practices and outcomes. This study is the first to investigate abortion-related mortality among FSW across eight countries: Angola, Brazil, Democratic Republic of Congo (DRC), India, Indonesia, Kenya, Nigeria, and South Africa. The Community Knowledge Approach (CKA) was used to survey a convenience sample of FSW (n = 1280). Participants reported on the deaths of peer FSW in their social networks during group meetings convened by non-governmental organisations (n = 165 groups, conducted across 24 cities in 2019). Details on any peer FSW deaths in the preceding five years were recorded. The circumstances of abortion-related deaths are reported here. Of the 1320 maternal deaths reported, 750 (56.8%) were due to unsafe abortion. The number of abortion-related deaths reported was highest in DRC (304 deaths reported by 270 participants), Kenya (188 deaths reported by 175 participants), and Nigeria (216 deaths reported by 312 participants). Among the abortion-related deaths, mean gestational age was 4.6 months and 75% occurred outside hospital. Unsafe abortion methods varied by country, but consumption of traditional or unknown medicines was most common (37.9% and 29.9%, respectively). The 750 abortion-related deaths led to 1207 children being left motherless. The CKA successfully recorded a stigmatised practice among a marginalised population, identifying very high levels of abortion-related mortality. Urgent action is now needed to deliver comprehensive sexual and reproductive healthcare to this vulnerable population, including contraception, safe abortion, and post-abortion care.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".