Causes of mortality among female sex workers: Results of a multi-country study
Bibliographic record
Abstract
Background: The vast majority of studies on female sex workers (FSW) focus on causes of morbidity while data on causes of mortality are scarce. In low- and middle-income countries, where civil registry and vital statistics data are often incomplete and FSW may not be identified as such in official registries, identifying causes of mortality among FSW has proven challenging. Methods: As part of a larger investigation on the maternal health of FSW, the current study used the Community Knowledge Approach (CKA) to identify causes of mortality among FSW in LMIC across three global regions in 2019. The CKA, validated to identify maternal, neonatal, and jaundice-associated deaths among women living in a community, was employed to identify deaths of any cause among communities of FSW. Study participants, recruited by in-country partner non-governmental organizations (NGOs) working with local FSW, provided detailed information about FSW deaths in their communities. Findings: 1280 FSW participated in 165 group meetings through which 2112 FSW deaths were identified. Of these reported deaths, 57·9% occurred in 2019 and 57·2% were among women aged 20-29. Causes of death included abortion (35·5%), other maternal causes (16·6%), suicide (13·6%), murder (12·5%), unclassified causes (11·6%), HIV/AIDS (7·9%), and accidents (3·2%). A total of 3659 children lost their mothers. Interpretation: Maternal death comprised the leading cause of FSW mortality in our sample. This methodology can be used by local governments and NGOs to identify unrecognized patterns and clusters of FSW deaths in near-real time and urgently steer targeted preventative strategies. Funding: New Venture Fund.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".