2.4. Data analysis
This study used a content analysis method, and the Krip pendorff’s thematic clustering technique was employed to analyze the data [30]. First, the interviews were thoroughly read to obtain a comprehensive understanding of the healthcare providers’ perceptions of barriers in implementing home telecare. Second, meaning units by sentence and paragraphs containing aspects related to the similar central meaning through context were extracted from the text, and then grouped thereby creating a cluster. The meaning units were condensed and summarized, and where possible described in terms of the underlying meaning, preserving the core content. By sorting all meaning units of similarity with the newly formed cluster, the condensed meaning units were abstracted into sub-themes. After repeated clustering, the data were presented in a tree diagram. We were able to abstract main themes and sub-themes. Each participant’s interview transcript was coded at the level of phrases and sentences. Before the classifications were confirmed, the data analysis was performed by the research team to discuss the fields of each theme, and the main themes and sub-themes were then identified, based on consensus. Fig. 1 illustrates an example of a dendrogram for one of the emerging themes.