Surviving pandemic control measures: The experiences of female sex workers during COVID-19 in Nairobi, Kenya
Bibliographic record
Abstract
At the beginning of the COVID-19 pandemic, the Kenya Ministry of Health instituted movement cessation measures and limits on face-to-face meetings. We explore the ways in which female sex workers (FSWs) in Nairobi were affected by the COVID-19 control measures and the ways they coped with the hardships. Forty-seven women were randomly sampled from the Maisha Fiti study, a longitudinal study of 1003 FSWs accessing sexual reproductive health services in Nairobi for an in-depth qualitative interview 4-5 months into the pandemic. We sought to understand the effects of COVID-19 on their lives. Data were transcribed, translated, and coded inductively. The COVID-19 measures disenfranchised FSWs reducing access to healthcare, decreasing income and increasing sexual, physical, and financial abuse by clients and law enforcement. Due to the customer-facing nature of their work, sex workers were hit hard by the COVID-19 restrictions. FSWs experienced poor mental health and strained interpersonal relationships. To cope they skipped meals, reduced alcohol use and smoking, started small businesses to supplement sex work or relocated to their rural homes. Interventions that ensure continuity of access to health services, prevent exploitation, and ensure the social and economic protection of FSWs during times of economic strain are required.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".