What information-related activities do people with ESKD use?

Background Information practice is an emerging area of research that seeks to reveal how people learn to connect with the complex multimodal information landscapes that informs their ability to make decisions. Previous research has identified that people with end stage kidney disease (ESKD) tend to adopt a ‘received’ or ‘engaged’ view of information but little is known about the activities of information practice. Objectives This research project sought to identify the: i) information-related activities; and ii) how information is used. Methods Using a constructivist qualitative methodology, ten people with ESKD living in a large metropolitan city were purposively selected and interviewed. Data was subject to thematic analysis by researchers from nursing and information science. Saturation of themes was achieved. Results Participants were between 38 and 72 years, had been receiving kidney replacement therapy from 2 weeks to 31 years. Eight participants reported having access to the internet but none participated in chat rooms. The activities were conceptualized into themes as listening, seeking, searching, sharing and observing. These activities enabled people to create, reflect on and evaluate the information needed to inform their decision-making Conclusion/Application to Clinical Practice The information practice research approach will enable a better understanding of the underlying relationship between information, knowledge and experience to be better understood. For renal nurses who are involved in patient education being able to recognise the way people use information will assist in individualizing educational sessions and tailoring teaching strategies to make it more meaningful

Assessing Poverty and Inequality at a Detailed Regional Level: New Advances in Spatial Microsimulation

poverty, inequality, measurement, Australia

Identifying long-term and imminent suicide predictors in a general population and a clinical sample with machine learning

Background: Machine learning (ML) is increasingly used to predict suicide deaths but their value for suicide prevention has not been established. Our first objective was to identify risk and protective factors in a general population. Our second objective was to identify factors indicating imminent suicide risk. Methods: We used survival and ML models to identify lifetime predictors using the Cohort of Norway (n=173,275) and hospital diagnoses in a Saskatoon clinical sample (n=12,614). The mean follow-up times were 17 years and 3 years for the Cohort of Norway and Saskatoon respectively. People in the clinical sample had a longitudinal record of hospital visits grouped in six-month intervals. We developed models in a training set and these models predicted survival probabilities in held-out test data. Results: In the general population, we found that a higher proportion of low-income residents in a county, mood symptoms, and daily smoking increased the risk of dying from suicide in both genders. In the clinical sample, the only predictors identified were male gender and older age. Conclusion: Suicide prevention probably requires individual actions with governmental incentives. The prediction of imminent suicide remains highly challenging, but machine learning can identify early prevention targets

Correlation of stress and predisposition in onset of illness in Masters of Social Work students

This study addressed the specific problem of whether there is a significant correlation between stress and the onset of predisposed disease. Because most graduate programs are stressful and it is known that several social work graduate students in one cohort at CSUSB were diagnosed with diabetes as well as migraines and depression, the population for this study was Master\u27s of Social Work (MSW) students at California State University, San Bernardino, (CSUSB)