Suicide during pregnancy as a major contributor to maternal suicide among female sex workers in eight low- and middle-income countries: A community knowledge approach investigation
Bibliographic record
Abstract
Studies indicate a high burden of mental health disorders among female sex workers (FSWs) in low- and middle-income countries (LMICs). Despite available data on suicidal ideation and suicide attempts among FSWs, little is known about suicide deaths in this hard-to-reach population. This study aims to examine the extent to which suicide is a cause of maternal mortality among FSWs, the contexts in which suicides occur, and the methods used. From January to October 2019, the Community Knowledge Approach method for identifying cause-specific deaths in communities was employed across eight LMICs (Angola, Brazil, the Democratic Republic of the Congo (DRC), India, Indonesia, Kenya, Nigeria, and South Africa). A total of one thousand two hundred eighty FSWs provided detailed reports on two thousand one hundred twelve FSW deaths in the preceding 5 years, including 288 (13.6%) suicides, 178 (61.8%) of which were maternal. Of these maternal suicides, 57.9% occurred during pregnancy (antepartum), 20.2% within two months of delivery (puerperium), and 21.9% in the 2-12 months following delivery (postpartum). The highest proportion of suicides occurred in Nigeria, Kenya, and DRC in sub-Saharan Africa. A total of 504 children lost their mothers to suicide. Further research is needed to identify interventions for suicide risk among FSW mothers.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".