dr lic. Adegboyega Ojo | Gdańsk University of Technology

Patient experience surveys have become a key source of evidence for supporting decision-making and continuous quality improvement within healthcare services. To harness free-text feedback collected as part of these surveys for additional insights, text analytics methods are increasingly employed when the data collected is not amenable to traditional qualitative analysis due to volume. However, while text analytics techniques offer...

National surveys on care experiences are increasingly adopted as regulatory mechanisms for improving care quality and increasing public trust in healthcare services. Based on data collected as part of Ireland's 2020 National Maternity Experience Survey, this study investigates care-related factors that contribute most to confidence and trust in the professional workforce (or carers) within Irish maternity services. The survey covered...

Background Patient experience surveys are a key source of evidence for supporting decision-making and quality improvement in healthcare services. These surveys contain two main types of questions: closed and open-ended, asking about patients’ care experiences. Apart from the knowledge obtained from analysing closed-ended questions, invaluable insights can be gleaned from free-text data. Advanced analytics techniques are increasingly...

Recent research has shown that organizational leaders’ tweets can influence employee anxiety. In this study, we turn the table and examine whether the same can be said about followers’ tweets. Based on emotional contagion and a dataset of 108 leaders and 178 followers across 50 organizations, we infer and track state- and trait-anxiety scores of participants over 316 days, including pre- and post the onset of the SARS-CoV-2 pandemic...

High-quality labelled datasets represent a cornerstone in the development of deep learning models for land use classification. The high cost of data collection, the inherent errors introduced during data mapping efforts, the lack of local knowledge, and the spatial variability of the data hinder the development of accurate and spatially-transferable deep learning models in the context of agriculture. In this paper, we investigate...