P6.003 Geographic Mapping of Female Sex Workers and Venue Profiling in Urban and Rural Districts of Zigong, Sichuan, China
Bibliographic record
Abstract
<h3>Introduction</h3> The purpose of this study is to geographically enumerate the population size of female sex workers (FSWs) and venue profiling of key venues, and to reveal the key elements which are related to the high risk sexual behaviours among FSWs in urban and rural areas of Zigong city in Sichuan, China. <h3>Methods</h3> Geographic mapping data were collected through systematically identifying hidden key venues in the rural and urban districts of Zigong. Venue profiling data were collected by interviewing key informants (KI) about the details of sex work operation, such as type of venue, duration of operation, operation days and time, peak days and time, services provided at each venue, number of clients on an average day and a peak day. To avoid social desirability bias of face-to-face interview, Polling Booth Survey was used to gather high risk sexual behaviours among FSWs (N = 60). <h3>Results</h3> A total of 324 key venues were mapped in Zigong. The key venues are massage parlours (108), teahouses (74) and small hotels (45), which accounting for 33.3%, 22.8% and 13.9% of total venues mapped. 112 venue KIs were interviewed and confirmed a total of 378 FSWs working in those 112 venues. The average number of FSWs per venue is 4. The age of the majority (80.4%) of FSWs was around 20 to 40 years old. The total estimated number of FSWs in Zigong is 1296. The sexual behaviours and operation patterns of key venues in urban and rural areas are different. Not consistent condom use, STI symptoms, and drug use are some typical high risk behaviours. <h3>Discussion and Conclusion</h3> Sex work industry is emerged in general social life in urban and rural China. The scope and operation of sex industry pose a special challenge to public health intervention programmes
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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 | 0.000 | 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".