HIV Surveillance and Research for Migrant Populations: Protocol Integrating Respondent-Driven Sampling, Case Finding, and Medicolegal Services for Venezuelans Living in Colombia
Bibliographic record
Abstract
BACKGROUND: Epidemiologic research among migrant populations is limited by logistical, methodological, and ethical challenges, but it is necessary for informing public health and humanitarian programming. OBJECTIVE: We describe a methodology to estimate HIV prevalence among Venezuelan migrants in Colombia. METHODS: Respondent-driven sampling, a nonprobability sampling method, was selected for attributes of reaching highly networked populations without sampling frames and analytic methods that permit estimation of population parameters. Respondent-driven sampling was modified to permit electronic referral of peers via SMS text messaging and WhatsApp. Participants complete sociobehavioral surveys and rapid HIV and syphilis screening tests with confirmatory testing. HIV treatment is not available for migrants who have entered Colombia through irregular pathways; thus, medicolegal services integrated into posttest counseling provide staff lawyers and legal assistance to participants diagnosed with HIV or syphilis for sustained access to treatment through the national health system. Case finding is integrated into respondent-driven sampling to allow partner referral. This study is implemented by a local community-based organization providing HIV support services and related legal services for Venezuelans in Colombia. RESULTS: Data collection was launched in 4 cities in July and August 2021. As of November 2021, 3105 of the target 6100 participants were enrolled, with enrollment expected to end by February/March 2022. CONCLUSIONS: Tailored methods that combine community-led efforts with innovations in sampling and linkage to care can aid in advancing health research for migrant and displaced populations. Worldwide trends in displacement and migration underscore the value of improved methods for translation to humanitarian and public health programming. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/36026.
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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 | 0.060 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".