Hidden Populations: Risk Behaviours in Drug-Using Populations in the Republic of Georgia Through Subsequent Peer-Driven Interventions
Bibliographic record
Abstract
Abstract BackgroundGeorgia has a significant risk of ongoing HIV and HCV outbreak. Within this context, harm reduction aims to reduce risk associated with drug use through community activities, such as peer recruitment and involvement. The aim of this study was to identify significant differences between known and hidden populations, and attest to the ongoing utility of peer-driven intervention across multiple years in recruiting high-risk, vulnerable populations through peer networks. It was hypothesised that significant differences would remain between known, and previously unknown, members of the drug-using community, and that peer-driven intervention would recruit individuals with high-risk, vulnerable individuals with significant differences to the known population.MethodsSampling occurred across 9 months in 11 cities in Georgia, recruiting a total of 2807 drug-using individuals. Standardised questionnaires were completed for all consenting and eligible participants, noting degree of involvement in harm reduction activities. This data underwent analysis to identify statistically significant different between those known and unknown to harm reduction activities, including in demographics, knowledge and risk behaviours.ResultsPeer recruitment was able to attract a significantly different cohort compared to those already known to harm reduction services. Peer-driven intervention was able to recruit a younger population, with 25.1% of PDI participants being under 25, compared to 3.2% of NSP participants. PDI successfully recruited women, with 6.9% of PDI participants being women compared to 2.0% in the NSP sample. Important differences in drug use, behaviour and risk were seen between the two groups, with the peer-recruited cohort undertaking higher-risk injecting behaviours. A mixture of risk differences was seen across different sub-groups and between the known and unknown population. Overall risk, driven by sex risk, was consistently higher in younger people (0.59 v. 0.57, p=0.00). Recent overdose was associated with higher risk in all risk categories. Regression showed age and location as important variables in overall risk. Peer-recruited individuals reported much lower rates of previous HIV testing (34.2% v. 99.5%, p=0.00). HIV knowledge and status were not significantly different.ConclusionsSignificant differences were seen between the known and unknown drug-using populations. The recruitment strategy was successful in recruiting females and younger people. This is especially important given that this sampling followed subsequent rounds of peer-driven intervention, implying the ability of peer-recruitment to consistently reach hidden, unknown populations of the drug using community, who have different risks and behaviours. Risk differences were seen compared to previous samples, lending strength to the peer-recruitment model, but also informing how harm reduction programmes should cater services, such as education, to different cohorts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".