Causes of preventable death among children of female sex worker mothers in low- and middle-income countries: A community knowledge approach investigation
Bibliographic record
Abstract
Background: Female sex workers (FSW) in low- and middle-income countries (LMIC) are disproportionately vulnerable to poor health, social, and economic outcomes. The children of female sex workers (CFSW) experience health risks based on these challenging circumstances and the unique conditions to which they are exposed. Although country child mortality data exist, little is known about the causes of death among CFSW specifically, thereby severely limiting an effective public health response to the needs of this high-risk group of children. Methods: The Community Knowledge Approach (CKA) was employed between January and October 2019 to survey a criterion sample of 1280 FSW participants across 24 cities in eight LMIC countries. Participants meeting pre-determined criteria provided detailed reports of deaths among the CFSW within their community of peers. Newborn deaths were gleaned from FSW maternal death reports where the infants also died following birth. Results: Of the 668 child deaths reported, 589 were included in the analysis. Nutritional deficiencies comprised the leading cause of mortality accounting for 20.7% of deaths, followed closely by accidents (20.0%), particularly house fires, overdoses (19.4%), communicable diseases (18.5%), and homicides (9.8%). Other reported causes of death included neonatal conditions, respiratory illnesses, and suicides. Conclusions: The causes of CFSW death in these eight countries are preventable with improved protections. Governments, intergovernmental organisations like the United Nations, nongovernmental stakeholder organisations (e.g. sex worker organisations), and funders can implement targeted policies and programmes to protect CFSW and assist vulnerable FSW who are pregnant and raising children. Further research is needed to identify effective child welfare safeguards for CFSW.
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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 | 0.003 | 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) | 0.002 | 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".