Longitudinal experiences and risk factors for common mental health problems and suicidal behaviours among female sex workers in Nairobi, Kenya
Bibliographic record
Abstract
Background: Female sex workers (FSWs) are at high risk of mental health problems and suicide risk. Few longitudinal studies have examined risk factors for poor mental health among FSWs. Methods: = 877) (June 2020-Jan 2021). Women reporting mental health problems were offered counselling services. Multivariable mixed logistic regression models were used to examine factors associated with mental health problems and suicidal behaviours. Results: There was a decline in the proportion of women reporting any mental health problem (depression and/or anxiety and/or PTSD) (baseline: 29.9%, midline: 13.3%, endline: 11.8%). There was strong evidence that any mental health problem was associated with recent hunger (aOR 1.99; 95% CI 1.37-2.88) and recent violence from non-intimate partners (2.23; 95% CI 1.55-3.19). Recent suicidal behaviour prevalence was similar across survey rounds (baseline: 10.2%; midline: 10.2%; endline: 10.4%), and was associated with recent violence from non-intimate partners (aOR 1.96; 95% CI 1.31-2.95), recent hunger (aOR 1.69; 95% CI 1.15-2.47) and having an additional employment to sex work (aOR 1.50; 95% CI 1.00-2.23). Conclusions: Our study found a decline in mental health problems but high levels of persistent suicidal behaviours among FSWs. Syndemic risk factors including food insecurity and violence were longitudinally associated with mental health problems and recent suicidal behaviours. There is a need for accessible mental health services for FSWs, alongside structural interventions addressing poverty and violence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".