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Summary: The aim of this study is to describe the impact of an education intervention programme on nurses'' willingness to care for HIV-positive people in Lithuania. Methods: The study utilizes a randomized controlled trial design (RCT). The total sample comprises 185 nurses working in medical, surgical and gynaecological units, and primary health care centres from the same hospital areas in three Lithuanian hospitals. The data were analyzed using SPSS 12.0 and descriptive statistics. Findings: Our educational intervention did not have an impact on the nurses'' willingness to take care of people living with HIV (PLHIV), as their level of willingness was high already before the education intervention. Conclusions: Further research on this issue is needed to try to understand the forces acting on our nursing staff in order to ensure appropriate care for PLHIV. [ABSTRACT FROM AUTHOR]

Background: Respondent driven sampling (RDS) and incentivized snowball sampling (ISS) are two sampling methods that are commonly used to reach people who inject drugs (PWID).Methods: We generated a set of simulated RDS samples on an actual sociometric ISS sample of PWID in Vilnius, Lithuania ("original sample") to assess if the simulated RDS estimates were statistically significantly different from the original ISS sample prevalences for HIV (9.8%), Hepatitis A (43.6%), Hepatitis B (Anti-HBc 43.9% and HBsAg 3.4%), Hepatitis C (87.5%), syphilis (6.8%) and Chlamydia (8.8%) infections and for selected behavioral risk characteristics.Results: The original sample consisted of a large component of 249 people (83% of the sample) and 13 smaller components with 1-12 individuals. Generally, as long as all seeds were recruited from the large component of the original sample, the simulation samples simply recreated the large component. There were no significant differences between the large component and the entire original sample for the characteristics of interest. Altogether 99.2% of 360 simulation sample point estimates were within the confidence interval of the original prevalence values for the characteristics of interest.Conclusions: When population characteristics are reflected in large network components that dominate the population, RDS and ISS may produce samples that have statistically non-different prevalence values, even though some isolated network components may be under-sampled and/or statistically significantly different from the main groups. This so-called "strudel effect" is discussed in the paper. [ABSTRACT FROM AUTHOR]