Degree and correlates of sexual mixing in female sex workers in Karnataka, India
Bibliographic record
Abstract
UNLABELLED: Background The degree of sexual mixing plays an important role in understanding disparities in sexually transmissible infections and HIV across social groups. This study examines the degree of sexual age mixing, and explores its individual and partnership level correlates among female sex workers (FSWs) in Karnataka, India. METHODS: Data were drawn from special behavioural surveys conducted in 2006-07 among 577 FSWs in two districts of Karnataka: Belgaum and Bangalore. Sexual mixing in age was assessed as the difference in age between FSWs and their sexual partners, and the degree of assortativeness in sexual mixing was assessed using Newman's assortativity coefficient. RESULTS: A total of 577 FSWs were interviewed; 418 of whom reported two or more partnerships, resulting in 942 partnerships. In about half (52%) of these partnerships, the age difference between the FSW and her sexual partner was 5 years or more. The degree of assortativity in age mixing was 0.098, indicating minimally assortative mixing. The disassortativeness in age mixing was positively associated with young age and no formal education, and negatively with duration in sex work. Partnerships which were of a commercial nature were more likely to be disassortative than noncommercial partnerships. CONCLUSION: The minimally assortative age mixing indicates sexually transmissible infections can transfer from members of one age group to another. Efforts are required to limit the transmission of infection from one group to other by promoting safer sexual behaviour.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".