MedT5SQL: a transformers-based large language model for text-to-SQL conversion in the healthcare domain.

Introduction: In response to the increasing prevalence of electronic medical records (EMRs) stored in databases, healthcare staff are encountering difficulties retrieving these records due to their limited technical expertise in database operations. As these records are crucial for delivering appropriate medical care, there is a need for an accessible method for healthcare staff to access EMRs. Methods: To address this, natural language processing (NLP) for Text-to-SQL has emerged as a solution, enabling non-technical users to generate SQL queries using natural language text. This research assesses existing work on Text-to-SQL conversion and proposes the MedT5SQL model specifically designed for EMR retrieval. The proposed model utilizes the Text-to-Text Transfer Transformer (T5) model, a Large Language Model (LLM) commonly used in various text-based NLP tasks. The model is fine-tuned on the MIMICSQL dataset, the first Text-to-SQL dataset for the healthcare domain. Performance evaluation involves benchmarking the MedT5SQL model on two optimizers, varying numbers of training epochs, and using two datasets, MIMICSQL and WikiSQL. Results: For MIMICSQL dataset, the model demonstrates considerable effectiveness in generating question-SQL pairs achieving accuracy of 80.63%, 98.937%, and 90% for exact match accuracy matrix, approximate string-matching, and manual evaluation, respectively. When testing the performance of the model on WikiSQL dataset, the model demonstrates efficiency in generating SQL queries, with an accuracy of 44.2% on WikiSQL and 94.26% for approximate string-matching. Discussion: Results indicate improved performance with increased training epochs. This work highlights the potential of fine-tuned T5 model to convert medical-related questions written in natural language to Structured Query Language (SQL) in healthcare domain, providing a foundation for future research in this area.

Mindfulness and technostress in the workplace: a qualitative approach.

Information Technology (IT) has been vastly characterized as a double-edged sword, offering significant benefits to individuals but at the same time bringing certain negative consequences, such as technostress. Technostress can severely affect individuals in the workplace, causing fatigue, loss of motivation, inability to concentrate, dissatisfaction at work and reduced productivity among others; thus significantly affecting individual well-being work as well as increasing costs for organisations. Recently, studies have shown the beneficial role of mindfulness in reducing technostress experiences of individuals; however, the evidence that exists until today is very limited, and mostly focused on evaluating the impact of mindfulness on technostress and its negative consequences. As the current research stands, at the moment it is relatively unknown how mindfulness affects the underlying mechanisms of technostress experiences of individuals. Through semi-structured interviews with 10 knowledge workers, the current study explores how mindfulness alleviates technostress within the workplace, by investigating the experiences of more mindful employees and learning from their practices. Findings offer a deeper insight into the relationship of mindfulness and technostress, revealing a toolkit of the underlying strategies that more mindful and IT mindful individuals deploy as well as their perceptions during technostress experiences at work thus shedding light on the path between mindfulness and technostress. The study contributes both to academia and practice, offering important implications to managers and practitioners that strive to improve employee well-being within organisations.

The Role of Mindfulness in Mitigating the Negative Consequences of Technostress.

IT offers significant benefits both to individuals and organisations, such as during the Covid-19 pandemic where technology played a primary role in aiding remote working environments; however, IT use comes with consequences such as 'technostress' - stress arising from extended use of technology. Addressing the paucity of research related to this topic, in this study, we examine the role of mindfulness and IT mindfulness to both mitigate the impact of technostress and alleviate its negative consequences; revealing that mindfulness can reduce technostress and increase job satisfaction, while IT mindfulness can enhance user satisfaction and improve task performance. Moreover, our work sheds light on the under-researched relationship between mindfulness and IT mindfulness; showing that the latter has a stronger influence on IT related outcomes; revealing the valuable role of mindfulness and IT mindfulness in the workplace and offering important implications to theory and practice.

Privacy and surveillance attitudes during health crises: Acceptance of surveillance and privacy protection behaviours.

The wide deployment of digital technologies for the management of the COVID-19 pandemic has triggered concerns about privacy and intrusion from government surveillance. This study investigates individual privacy and surveillance attitudes by developing a theoretical model to explain acceptance of government surveillance and privacy protection behaviours during health-crises, such as the COVID-19 pandemic. Results from a US sample reveal that people are concerned about the collection and use of their personal information via mobile applications and the monitoring of their online activities by authorities. Findings reveal the important roles of political trust and belief that governments' need to be proactive in protecting peoples' welfare during a crisis that can increase acceptance of surveillance and thus assist in the management of the health crisis. Implications for research and practice are discussed.

Privacy nudges for disclosure of personal information: A systematic literature review and meta-analysis.

OBJECTIVE: Digital nudging has been mooted as a tool to alter user privacy behavior. However, empirical studies on digital nudging have yielded divergent results: while some studies found nudging to be highly effective, other studies found no such effects. Furthermore, previous studies employed a wide range of digital nudges, making it difficult to discern the effectiveness of digital nudging. To address these issues, we performed a systematic review of empirical studies on digital nudging and information disclosure as a specific privacy behavior. METHOD: The search was conducted in five digital libraries and databases: Scopus, Google Scholar, ACM Digital Library, Web of Science, and Science Direct for peer-reviewed papers published in English after 2006, examining the effects of various nudging strategies on disclosure of personal information online. RESULTS: The review unveiled 78 papers that employed four categories of nudge interventions: presentation, information, defaults, and incentives, either individually or in combination. A meta-analysis on a subset of papers with available data (n = 54) revealed a significant small-to-medium sized effect of the nudge interventions on disclosure (Hedges' g = 0.32). There was significant variation in the effectiveness of nudging (I2 = 89%), which was partially accounted for by interventions to increase disclosure being more effective than interventions to reduce disclosure. No evidence was found for differences in the effectiveness of nudging with presentation, information, defaults, and incentives interventions. CONCLUSION: Identifying ways to nudge users into making more informed and desirable privacy decisions is of significant practical and policy value. There is a growing interest in digital privacy nudges for disclosure of personal information, with most empirical papers focusing on nudging with presentation. Further research is needed to elucidate the relative effectiveness of different intervention strategies and how nudges can confound one another.