Qualitative Research in Healthcare: Data Analysis / 예방의학회지

Qualitative research methodology has been applied with increasing frequency in various fields, including in healthcare research, where quantitative research methodology has traditionally dominated, with an empirically driven approach involving statistical analysis. Drawing upon artifacts and verbal data collected from in-depth interviews or participatory observations, qualitative research examines the comprehensive experiences of research participants who have experienced salient yet unappreciated phenomena. In this study, we review 6 representative qualitative research methodologies in terms of their characteristics and analysis methods: consensual qualitative research, phenomenological research, qualitative case study, grounded theory, photovoice, and content analysis. We mainly focus on specific aspects of data analysis and the description of results, while also providing a brief overview of each methodology’s philosophical background. Furthermore, since quantitative researchers have criticized qualitative research methodology for its perceived lack of validity, we examine various validation methods of qualitative research. This review article intends to assist researchers in employing an ideal qualitative research methodology and in reviewing and evaluating qualitative research with proper standards and criteria.

Updating Korean Disability Weights for Causes of Disease: Adopting an Add-on Study Method / 예방의학회지

Objectives@#Disability weights require regular updates, as they are influenced by both diseases and societal perceptions. Consequently, it is necessary to develop an up-to-date list of the causes of diseases and establish a survey panel for estimating disability weights. Accordingly, this study was conducted to calculate, assess, modify, and validate disability weights suitable for Korea, accounting for its cultural and social characteristics. @*Methods@#The 380 causes of disease used in the survey were derived from the 2019 Global Burden of Disease Collaborative Network and from 2019 and 2020 Korean studies on disability weights for causes of disease. Disability weights were reanalyzed by integrating the findings of an earlier survey on disability weights in Korea with those of the additional survey conducted in this study. The responses were transformed into paired comparisons and analyzed using probit regression analysis. Coefficients for the causes of disease were converted into predicted probabilities, and disability weights in 2 models (model 1 and 2) were rescaled using a normal distribution and the natural logarithm, respectively. @*Results@#The mean values for the 380 causes of disease in models 1 and 2 were 0.488 and 0.369, respectively. Both models exhibited the same order of disability weights. The disability weights for the 300 causes of disease present in both the current and 2019 studies demonstrated a Pearson correlation coefficient of 0.994 (p=0.001 for both models). This study presents a detailed add-on approach for calculating disability weights. @*Conclusions@#This method can be employed in other countries to obtain timely disability weight estimations.