Search Articles

View query in Help articles search

Search Results (1 to 3 of 3 Results)

Download search results: CSV END BibTex RIS


A Web-Based, Respondent-Driven Sampling Survey Among Men Who Have Sex With Men (Kai Noi): Description of Methods and Characteristics

A Web-Based, Respondent-Driven Sampling Survey Among Men Who Have Sex With Men (Kai Noi): Description of Methods and Characteristics

Our goal was to design and conduct a web RDS among MSM in Thailand. The objectives were 2-fold: to create a ready-to-use (coded) web RDS system and to pilot the feasibility of collecting HIV-related biomarkers through such a sampling design. There was no physical survey office.

Samart Karuchit, Panupit Thiengtham, Suvimon Tanpradech, Watcharapol Srinor, Thitipong Yingyong, Thananda Naiwatanakul, Sanny Northbrook, Wolfgang Hladik

JMIR Form Res 2024;8:e50812

Accumulation of Biological and Behavioral Data of Female Sex Workers Using Respondent-Driven Sampling: Protocol for a Systematic Review

Accumulation of Biological and Behavioral Data of Female Sex Workers Using Respondent-Driven Sampling: Protocol for a Systematic Review

The respondent-driven sampling (RDS) method is a nonprobability sampling method that approximates probability sample design, allowing for the extrapolation of results to the target population. This method is generally used to address the limitations of studying hidden or hard-to-reach populations [1].

Mihir Bhatta, Agniva Majumdar, Sitikantha Banerjee, Piyali Ghosh, Subrata Biswas, Shanta Dutta

JMIR Res Protoc 2023;12:e43722

Population Size Estimation of Men Who Have Sex With Men in Rwanda: Three-Source Capture-Recapture Method

Population Size Estimation of Men Who Have Sex With Men in Rwanda: Three-Source Capture-Recapture Method

Capture two was followed by the Integrated Behavioral and Biological Surveillance Survey (IBBSS) using RDS [32], which served as the opportunity for the third capture. RDS is a variant of chain-referral sampling that employs Markov-chain theory and the theory of biased networks to reduce biases generally associated with chain-referral methods [31].

Elysee Tuyishime, Catherine Kayitesi, Gentille Musengimana, Samuel Malamba, Hailegiorgis Moges, Ida Kankindi, Horacio Ruisenor Escudero, Ignace Habimana Kabano, Tom Oluoch, Eric Remera, Angela Chukwu

JMIR Public Health Surveill 2023;9:e43114