Violence across the Life Course and Implications for Intervention Design: Findings from the Maisha Fiti Study with Female Sex Workers in Nairobi, Kenya
Bibliographic record
Abstract
We examined violence experiences among Female Sex Workers (FSWs) in Nairobi, Kenya, and how these relate to HIV risk using a life course perspective. Baseline behavioural-biological surveys were conducted with 1003 FSWs June-December 2019. Multivariable logistic regression models were used to estimate the adjusted odds ratio (AOR) and 95% confidence intervals (CI) for associations of life course factors with reported experience of physical or sexual violence in the past 6 months. We found substantial overlap between violence in childhood, and recent intimate and non-intimate partner violence in adulthood, with 86.9% reporting one or more types of violence and 18.7% reporting all three. Recent physical or sexual violence (64.9%) was independently associated with life course factors, including a high WHO Adverse Childhood Experiences (ACE) score (AOR = 7.92; 95% CI:4.93-12.74) and forced sexual debut (AOR = 1.97; 95% CI:1.18-3.29), as well as having an intimate partner (AOR = 1.67; 95% CI:1.25-2.23), not having an additional income to sex work (AOR = 1.54; 95% CI:1.15-2.05), having four or more dependents (AOR = 1.52; 95% CI:0.98-2.34), recent hunger (AOR = 1.39; 95% CI:1.01-1.92), police arrest in the past 6 months (AOR = 2.40; 95% CI:1.71-3.39), condomless last sex (AOR = 1.46; 95% CI:1.02-2.09), and harmful alcohol use (AOR = 3.34; 95% CI:1.74-6.42). Interventions that focus on violence prevention during childhood and adolescence should help prevent future adverse trajectories, including violence experience and HIV acquisition.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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".