Cohort profile: the Kyrgyzstan InterSectional Stigma (KISS) injection drug use cohort study
Bibliographic record
Abstract
BACKGROUND: In Kyrgyzstan and other Eastern European and Central Asian countries, injection drug use and HIV-related intersectional stigma undermines HIV prevention efforts, fueling a rapidly expanding HIV epidemic. The Kyrgyzstan InterSectional Stigma (KISS) Injection Drug Use Cohort is the first study designed to assess the impact of drug use, methadone maintenance treatment (MMT) and HIV stigma experiences among people who inject drugs (PWID) on HIV prevention service utilization. METHODS: Adult PWID were recruited from Bishkek city and the surrounding rural Chuy Oblast region in northern Kyrgyzstan via modified time location sampling and snowball sampling. All participants completed a baseline rapid HIV test and interviewer-administered survey. A subsample of participants were prospectively followed for three months and surveyed to establish retention rates for future work in the region. Internal reliability of three parallel stigma measures (drug use, MMT, HIV) was evaluated. Descriptive statistics characterize baseline experiences across these three stigma types and HIV prevention service utilization, and assess differences in these experiences by urbanicity. RESULTS: The KISS cohort (N = 279, 50.5% Bishkek, 49.5% Chuy Oblast) was mostly male (75.3%), ethnically Russian (53.8%), median age was 40 years old (IQR 35-46). Of the 204 eligible participants, 84.9% were surveyed at month 3. At baseline, 23.6% had a seropositive rapid HIV test. HIV prevention service utilization did not differ by urbanicity. Overall, we found 65.9% ever utilized syringe service programs in the past 6 months, 8.2% were utilizing MMT, and 60.8% met HIV testing guidelines. No participants reported PrEP use, but 18.5% had heard of PrEP. On average participants reported moderate levels of drug use (mean [M] = 3.25; α = 0.80), MMT (M = 3.24; α = 0.80), and HIV stigma (M = 2.94; α = 0.80). Anticipated drug use stigma from healthcare workers and internalized drug use stigma were significantly higher among PWID from Bishkek (p < 0.05), while internalized HIV stigma among PWID living with HIV was significantly greater among PWID from Chuy Oblast (p = 0.03). CONCLUSION: The KISS cohort documents moderate levels of HIV-related intersectional stigma and suboptimal engagement in HIV prevention services among PWID in Kyrgyzstan. Future work will aim identify priority stigma reduction intervention targets to optimize HIV prevention efforts in the region.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".