P1-S4.13 Mapping high risk activities of HIV/AIDS in Gaoxin and Yantan District of Zigong City
Bibliographic record
Abstract
Background Zigong is located in the south of Sichuan Province of China. As a key area with a large number of migrants, Zigong has long been confronting the danger of HIV/AIDS. The goal of “Zigong geographic mapping on HIV/AIDS high-risk population” research project is to provide information on the location, type and volume of the female sex workers (FSWs) in Gaoxin (urban) and Yantan District (rural) to provide baseline information for HIV/AIDS prevention policy and programs in future. Methods This study adopted a “geographical approach” to map the location and spots of the activities of sex trade and estimated the number of FSWs involved in the activities. This included two sequential steps: 1) Systematic information gathering from key informants (KI) suggested the locations (“hot spots”) where FSWs congregate. 2) The “hot spots” were validated through site visit and insiders; the information about the number and characteristics of FSWs in each spot were collected. Results In Gaoxin District: 59 high-risk spots were confirmed and 16 clusters were marked in 10 zones. The most common type of sex trade spots was hair salon/massage room/foot massage room. 72.9% of the spots were both “seeking risk” and “taking risk”, while 22.0% and 3.4% were only “seeking risk” and “taking risk” respectively. 39.0% of the spots had more than three clients per FSW per day. The estimated number of total FSWs in this urban area was 303. 38.5% of FSWs were in hotel/small lodge, while 29.3% and 27.3% were in small tea house/bar/KTV and hair salon/massage room/foot massage room respectively. The peak season, peak date and peak time of the most spots was summer, the whole week, afternoon and night. In Yantan District: 12 high-risk spots were confirmed and half were concentrated in Yantan Town. The most common type was small tea house/bar/KTV. Nine spots were both “seeking risk” and “taking risk”, while three were “taking risk” only. Five spots had more than three clients per day for each FSW. The estimated number of FSWs was 42, and 74.4% worked in the small tea house/bar/KTV. The peak season, peak date and peak time of the most spots was spring and summer, the whole week, and night, respectively. Conclusions The mapping approach provided direct and visible geographic distribution information, which enables a quick mastery of the distribution of high risk spots and the number of high risk population, for public health intervention planning and program implementation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".