Beaten but not down! Exploring resilience among female sex workers (FSWs) in Nairobi, Kenya
Bibliographic record
Abstract
BACKGROUND: In Kenya sex work is illegal and those engaged in the trade are stigmatized and marginalized. We explored how female sex workers in Nairobi, Kenya, utilize different resources to navigate the negative consequences of the work they do. METHODS: Qualitative data were collected in October 2019 from 40 FSWs who were randomly sampled from 1003 women enrolled in the Maisha Fiti study, a 3-year longitudinal mixed-methods study exploring the relationship between HIV risk and violence and mental health. All interviews were audio-recorded, transcribed and translated. Data were thematically coded and analyzed using Nvivo 12. RESULTS: Participants' age range was 18-45 years. Before entry into sex work, all but one had at least one child. Providing for the children was expressed as the main reason the women joined sex work. All the women grew up in adverse circumstances such as poor financial backgrounds and some reported sexual and physical abuse as children. They also continued to experience adversity in their adulthood including intimate partner violence as well as violence at the workplace. All the participants were noted to have utilised the resources they have to build resilience and cope with these adversities while remaining hopeful for the future. Motherhood was mentioned by most as the reason they have remained resilient. Coming together in groups and engaging with HIV prevention and treatment services were noted as important factors too in building resilience. CONCLUSION: Despite the adverse experiences throughout the lives of FSWs, resilience was a key theme that emerged from this study. A holistic approach is needed in addressing the health needs of female sex workers. Encouraging FSWs to come together and advocating together for their needs is a key resource from which resilience and forbearance can grow. Upstream prevention through strengthening of education systems and supporting girls to stay in school and complete their secondary and/or tertiary education would help them gain training and skills, providing them with options for income generation during their adult lives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".