P2-S2.08 Changing patterns and drivers of migration among female sex workers of Northern Karnataka to large cities of Maharashtra, India in the context of HIV/AIDS
Bibliographic record
Abstract
Background and Objectives The large number of FSWs from the rural areas of Northern Karnataka's three districts namely Bagalkot, Belgaum and Bijapur (3B districts) who migrate and work in Maharashtra cities particularly in Solapur, Poona, Bhiwandi and Mumbai in brothels and lodges. In this corridor” of migration and interconnected HIV epidemics, an attempt is made in this paper to specifically address the following objectives: 1. To assess the volume of sex worker migration from different sites and its annual turnover. 2. To describe the patterns of sex worker migration to and from the three districts of northern Karnataka and the large urban centres of Maharashtra. Methods Mapping was conducted following enumeration of the units; visited each unit (brothels and in some areas also lodges and/or dhabas) where we had prior information that sex work was conducted and interviewed the unit manager (ie, brothel madam, lodge/ dhaba manager). The manager provided information regarding: the number of FSWs working in the unit at the time—total number, FSWs from Karnataka, FSWs from the 3B districts; number of FSWs from the 3B districts that worked in the unit in the previous year and how many of them had moved to another unit in the same area. Moreover, managers provided the place of origin, age and duration of stay in the area of the FSWs from the 3B districts working in the unit at the time of data collection. Results In case of Bhivandi, Mumbai's Kamathipur and Poona, 571, 390 and 604 FSWs were from 3B districts respectively. There were 338 women working in Solapur. Of the 338 women, 127 were from Karnataka and all most all of them (114) from the 3B districts. Conclusions Migration of FSWs from 3B districts is decreasing considerably to Maharashtra brothels. There is high turnover of FSWs from 3B districts in Solapur (100% per month), especially in lodges. This suggests that the proximity of Solapur to Bijapur (the main place of origin for FSWs in this place of destination) may be an important driver of the movement of FSWs. In other words, the difference in turnover between Solapur and the other places of destination may be indicative of two changes in the migration pattern: a) FSWs from the 3B districts may prefer closer destinations, rather than the “classic brothel based” destinations (eg, Mumbai, Pune, Bhiwandi due to declining clients); and b) FSWs from the 3B district who migrate to nearby destinations may prefer working in lodges rather than brothels, the “classic” place of sex work in Maharashtra where high prevalence of HIV/AIDS is observed. This is supported by mean ages of the FSWs (31.5, 28.3, 34.5 and 27.6) and mean durations of stay (10.5, 6.8, 11.9 and 4.0) in Bhiwandi, Pune, Mumbai and Solapur respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".