Caregiver burden and resilience among Malaysian caregivers of individuals with severe mental illness

Little research has focused on caregiver burden experienced by Malaysian caregivers of individuals with mental illness, despite the fact that data in the Asian region shows almost threequarter of patients with mental illness live with family members. The aim of this research was to examine the levels of caregiver burden and resilience of caregivers of individuals with severe mental illness and to determine the influencing factors on caregiver burden. A quantitative, cross sectional, correlational design was used to measure burden and resilience and to explore the relationship between demographic variables, caregiver stressors, resilience and caregiver burden. This study was guided by the model of Carer Stress and Burden. Data collection was conducted over two months in summer 2014. A self-administered questionnaire that consisted of four sections measuring demographic data, primary stressors, caregiver burden and resilience was used to collect data. Two hundred and one caregivers of individuals with mental illness attending Psychiatric Outpatient Clinics in Malaysia were recruited. Samples were selected using non-probability, consecutive sampling. Factors that were found to be significantly associated with caregiver burden were caregivers’ age, gender, ethnic group, employment status, having a medical condition and current health status. The primary stressors found to be significantly associated with caregiver burden include the time spent for caregiving tasks, unavailability of support with caregiving tasks, lack of emotional support and patients’ behavioural disturbances. In addition, it was found that caregivers who were less resilient reported a higher level of caregiver burden. Findings from hierarchical multiple regression indicated that caregivers’ marital status, current health status, time spent for caregiving and resilience predicted caregiver burden. This research provides insight into caregiver burden among caregivers of individuals with mental illness in Malaysia. It highlights the important factors associated with caregiver burden and the significant role of resilience in reducing caregiver burden.

Towards a model for exploring the relationship between managerial decision problems and decision support opportunities

The organisational decision making environment is complex, and decision makers must deal with uncertainty and ambiguity on a continuous basis. Managing and handling decision problems and implementing a solution, requires an understanding of the complexity of the decision domain to the point where the problem and its complexity, as well as the requirements for supporting decision makers, can be described. Research in the Decision Support Systems domain has been extensive over the last thirty years with an emphasis on the development of further technology and better applications on the one hand, and on the other hand, a social approach focusing on understanding what decision making is about and how developers and users should interact. This research project considers a combined approach that endeavours to understand the thinking behind managers’ decision making, as well as their informational and decisional guidance and decision support requirements. This research utilises a cognitive framework, developed in 1985 by Humphreys and Berkeley that juxtaposes the mental processes and ideas of decision problem definition and problem solution that are developed in tandem through cognitive refinement of the problem, based on the analysis and judgement of the decision maker. The framework facilitates the separation of what is essentially a continuous process, into five distinct levels of abstraction of manager’s thinking, and suggests a structure for the underlying cognitive activities. Alter (2004) argues that decision support provides a richer basis than decision support systems, in both practice and research. The constituent literature on decision support, especially in regard to modern high profile systems, including Business Intelligence and Business analytics, can give the impression that all ‘smart’ organisations utilise decision support and data analytics capabilities for all of their key decision making activities. However this empirical investigation indicates a very different reality.