Students who self-harm: coping style, rumination and alexithymia

Counsellors working with students or other young adults may encounter individuals who have self-harmed, either with suicidal or non-suicidal intent. Recent US studies reported rates of self-injury of up to 37% of the student population, but studies in the UK have focussed primarily on younger adolescents. This study examined reported self-harm incidents (scratching, cutting, poisoning, overdose etc) from a sample of 617 university students. A total of 27% reported at least one incident of self-harm, with almost 10% having harmed themselves while at university. Gender differences were not significant but psychology students reported significantly more self-harm than other students. Participants reporting self-harm scored significantly higher on maladaptive coping styles, rumination, and alexithymia (specifically difficulty in identifying emotions) and these differences were most marked for students reporting repetitive and recent self-harm. Rumination and Alexithymia factor 1 (difficulty identifying feelings) emerged as the most robust factors predicting self-harm status. Comments from students who self-harmed at university highlighted the importance of accessible services and academic staff support. The implications of these findings for counselling interventions are discussed, including challenging negative rumination tendencies and developing mindfulness skills.

A review of dynamic Bayesian network techniques with applications in healthcare risk modelling

Coping with an ageing population is a major concern for healthcare organisations around the world. The average cost of hospital care is higher than social care for older and terminally ill patients. Moreover, the average cost of social care increases with the age of the patient. Therefore, it is important to make efficient and fair capacity planning which also incorporates patient centred outcomes. Predictive models can provide predictions which their accuracy can be understood and quantified. Predictive modelling can help patients and carers to get the appropriate support services, and allow clinical decision-makers to improve care quality and reduce the cost of inappropriate hospital and Accident and Emergency admissions. The aim of this study is to provide a review of modelling techniques and frameworks for predictive risk modelling of patients in hospital, based on routinely collected data such as the Hospital Episode Statistics database. A number of sub-problems can be considered such as Length-of-Stay and End-of-Life predictive modelling. The methodologies in the literature are mainly focused on addressing the problems using regression methods and Markov models, and the majority lack generalisability. In some cases, the robustness, accuracy and re-usability of predictive risk models have been shown to be improved using Machine Learning methods. Dynamic Bayesian Network techniques can represent complex correlations models and include small probabilities into the solution. The main focus of this study is to provide a review of major time-varying Dynamic Bayesian Network techniques with applications in healthcare predictive risk modelling.

The Role of Emotion in Global Warming Policy Support and Opposition

Prior research has found that affect and affective imagery strongly influence public support for global warming. This article extends this literature by exploring the separate influence of discrete emotions. Utilizing a nationally representative survey in the United States, this study found that discrete emotions were stronger predictors of global warming policy support than cultural worldviews, negative affect, image associations, or sociodemographic variables. In particular, worry, interest, and hope were strongly associated with increased policy support. The results contribute to experiential theories of risk information processing and suggest that discrete emotions play a significant role in public support for climate change policy. Implications for climate change communication are also discussed.