Most people with dementia (PwD) are cared for by unpaid family carers, many of whom must balance caring with paid work. This regularly entails dealing with care-related emergencies (CRE). This study aims to explore the impact of carers’ autonomy at work regarding breaks, schedule, and place on their ability to manage CRE, and use technology to that end. We conducted interviews with 16 working carers of PwD in Scotland. Data were analysed thematically to identify key themes. Autonomy at work appeared on a spectrum from no to complete autonomy. Carers’ position on this spectrum was often dynamic and determined by the nature of their work, their workplace culture and regulations, and their line managers’ support – or clients in the case of self-employed carers. Break autonomy allowed carers to use technology to be notified of and delegate the CRE response. Schedule autonomy allowed for an in-person response to CRE. Place autonomy allowed carers to work and care simultaneously, which enabled them to manage CRE immediately but presented them with additional challenges. Distance between workplace and PwD’s residence impacted carers’ ability to manage CRE, despite having complete autonomy. Implications for healthcare professionals, service providers, employers, policymakers, and technology developers are presented.
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Proper decision-making is one of the most important capabilities of an organization. Therefore, it is important to have a clear understanding and overview of the decisions an organization makes. A means to understanding and modeling decisions is the Decision Model and Notation (DMN) standard published by the Object Management Group in 2015. In this standard, it is possible to design and specify how a decision should be taken. However, DMN lacks elements to specify the actors that fulfil different roles in the decision-making process as well as not taking into account the autonomy of machines. In this paper, we re-address and-present our earlier work [1] that focuses on the construction of a framework that takes into account different roles in the decision-making process, and also includes the extent of the autonomy when machines are involved in the decision-making processes. Yet, we extended our previous research with more detailed discussion of the related literature, running cases, and results, which provides a grounded basis from which further research on the governance of (semi) automated decision-making can be conducted. The contributions of this paper are twofold; 1) a framework that combines both autonomy and separation of concerns aspects for decision-making in practice while 2) the proposed theory forms a grounded argument to enrich the current DMN standard.
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