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. 2025 Jan 24;22(2):156.
doi: 10.3390/ijerph22020156.

Measuring Navigational Health Literacy in Russia: Validation of the HLS19-NAV-RU

Oxana Drapkina  1 , Artemii Molosnov  2   3 , Denis Tyufilin  2 , Maria Lopatina  1 , Viktor Medvedev  2 , Valeriya Chigrina  2 , Olga Kobyakova  2 , Ivan Deev  4 , Lennert Griese  5 , Doris Schaeffer  5 , Robert Griebler  6 , Polina Tuillet  7 , Anna Kontsevaya  1
Affiliations

Measuring Navigational Health Literacy in Russia: Validation of the HLS19-NAV-RU

Oxana Drapkina et al. Int J Environ Res Public Health. .

Abstract

Structures and regulations of healthcare systems in many countries have become increasingly complex and difficult for patients and users to navigate. Thus, more than ever before, navigational health literacy (NAV-HL) is needed by patients. There are no data on NAV-HL in Russia due to the lack of suitable concepts and measuring tools. Therefore, the study aimed to validate the HLS19 navigational health literacy assessment tool (HLS19-NAV) for the Russian-speaking population. This study provides a comprehensive overview of the validation process, including a comprehensibility check and psychometric analysis. Overall, the results of the HLS19-NAV-RU validation demonstrate the partial validity of the NAV-HL tool in the Russian language. Nevertheless, the instrument can be recommended for further research and use in health literacy studies in Russia.

Keywords: Russia; health literacy; navigational; population; questionnaire; validation.

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Conflict of interest statement

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Confirmatory factor analysis (2-factor model). The circles represent latent factors, while the rectangles correspond to observed variables. The lines connecting the latent factors to the observed variables are standardized factor loadings. Dot lines indicate fixed parameters used for scaling by fixing factor loadings. The arrow between the two latent factors represents the covariance of the latent constructs. Arrows from the latent factors to themselves denote the latent factor variances, and arrows from the observed variables to themselves represent the variances of the observed variables.
Figure 2
Figure 2
Scree plot. The circles represent the eigenvalues calculated from the data. The green triangles indicate the results of parallel analysis, which compare the observed eigenvalues with those generated from random data. The red line labeled “OC” marks the optimal coordinates, which corresponds to an extrapolation of the preceding eigenvalue by a regression line between the eigenvalue coordinates and the last eigenvalue coordinates [17]. The blue dot labeled “AF” represents the acceleration factor. These methods identify the number of components to retain in a factor analysis. The analysis was conducted using Spearman correlations, with 1000 random data replications for parallel analysis at the 95th percentile quantile.
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