ABSTRACT
Online mental health interventions have been posited as a way to reduce the mental health treatment gap among students in higher education. The effectiveness of these interventions is often limited by low user adherence. A potential solution is to improve user adherence by producing user-centred interventions. A total of 452 students from University College Cork, Ireland participated in the survey, "Tell us About Your Mental Health Post-COVID-19”. The survey examined students' mental health over the past year, their use of technological supports, their use of mental health support services and their ratings of mental health support services used. This study explores students' experiences with technological support. The thematic analysis of 138 open-ended responses produced seven main themes: 1) Appeal 2) Barriers to Use 3) Discovery 4) Drawbacks 5) Purpose 6) Reasons for Stopping and 7) Usage Patterns. The results of this study revealed students' openness to using online mental health resources. It also revealed the barriers and facilitators to their use of these resources. Finally, based on our findings, we provide recommendations to researchers/designers developing online mental health interventions for university students. Some of these recommendations were to ensure safety in online communities, provide good user interfaces, support students in crises and improve the accessibility of online resources to students with learning disabilities. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
A limited number of studies have been conducted to investigate the dynamics of COVID-19 disease spread in South Africa and these existing studies have mostly focussed on mathematical analysis of a relatively short time period near the initial outbreak of COVID-19 in South Africa. The current study therefore attempted to extend on previous studies by applying a Susceptible- Exposed - Infected - Removed (SEIR) disease model to analyse the long-term dynamics of COVID-19 in South Africa, taking into account multiple waves of infection potentially caused by different virus strains. A Differential Evolution (DE) algorithm was used to fit the proposed model to real-world data, and this was done on both a geographically local and global scale to investigate the differences between these two approaches. Results revealed that a local approach provided a more accurate model fit to data than a global approach and that the method proposed in this work could give valuable insights into disease dynamics. © 2022 IEEE.