Programmatic mapping and size estimation of female sex workers, transgender sex workers and men who have sex with men in İstanbul and Ankara, Turkey
Bibliographic record
Abstract
OBJECTIVES: Despite a growing HIV threat, there is no definition and characterisation of key populations (KPs), who could be the major drivers of the epidemic in Turkey. We used programmatic mapping to identify locations where KPs congregate, estimate their numbers and understand their operational dynamics to develop appropriate HIV programme implementation strategies. METHODS: Female and transgender sex workers (FSWs and TGSWs), and men who have sex with men (MSM) were studied in İstanbul and Ankara. Within each district, hot spots were identified by interviewing key informants and a crude spot list in each district was developed. The spot validation process was led by KP members who facilitated spot access and interviews of KPs associated with that spot. Final estimates were derived by aggregating the estimated number of KPs at all spots, which was adjusted for the proportion of KPs who visit multiple spots, and for the proportion of KPs who do not visit spots. RESULTS: FSWs were the largest KP identified in İstanbul with an estimate of 30 447 (5.8/1000 women), followed by 15 780 TGSWs (2.9/1000 men) and 11 656 MSM (2.1/1000). The corresponding numbers in Ankara were 9945 FSWs (5.2/1000 women), 1770 TGSWs (1/1000 men) and 5018 MSM (2.5/1000 men). Each KP had unique typologies based on the way they find and interact with sex partners. MSM were mostly hidden and a higher proportion operated through internet and phone-based applications. Night time was the peak time with Friday, Saturday and Sunday being the peak days of activity in both İstanbul and Ankara. CONCLUSIONS: This study has highlighted the presence of a substantial number of FSWs, TGSW and MSM in İstanbul and Ankara. The information obtained from this study can be used to set priorities for resource allocation and provide HIV prevention services where coverage could be the highest.
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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 | 0.001 | 0.001 |
| 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".