Objective: To gain insight into how communication vulnerable people and health-care professionals experience the communication in dialogue conversations, and how they adjust their conversation using augmentative and alternative communication (AAC) or other communication strategies. Methods: Communication vulnerable clients and health-care professionals in a long-term care institution were observed during a dialogue conversation (n = 11) and subsequently interviewed (n = 22) about their experiences with the conversation. The clients had various communication difficulties due to different underlying aetiologies, such as acquired brain injury or learning disorder. Results from the observations and interviews were analysed using conventional content analysis. Results: Seven key themes emerged regarding the experiences of clients and professionals: clients blame themselves for miscommunications; the relevance of both parties preparing the conversation; a quiet and familiar environment benefitting communication; giving clients enough time; the importance and complexity of nonverbal communication; the need to tailor communication to the client; prejudices and inexperience regarding AAC. The observations showed that some professionals had difficulties using appropriate communication strategies and all professionals relied mostly on verbal or nonverbal communication strategies. Conclusion: Professionals were aware of the importance of preparation, sufficient time, a suitable environment and considering nonverbal communication in dialogue conversations. However, they struggled with adequate use of communication strategies, such as verbal communication and AAC. There is a lack of knowledge about AAC, and professionals and clients need to be informed about the potential of AAC and how this can help them achieve equal participation in dialogue conversations in addition to other communication strategies.
In deze bijdrage wordt verslag gedaan van de afstudeertafels van het CBSS 2020 experiment, waar 29 studenten communicatie en international communication en 5 studenten van de Academie Minerva in deelnamen
Purpose – This paper aims to introduce the special issue on CSR communication attached to the First International CSR Communication Conference held in Amsterdam in October 2011. The aim of the introduction is also to review CSR communication papers published in scholarly journals in order to make a summary of the state of CSR communication knowledge. Design/methodology/approach – The existing literature on CSR communication was approached via systematic review. with a combination of conventional and summative qualitative content analysis. The final dataset contained 90 papers from two main business and management databases, i.e. EBSCOhost and ProQuest.Findings – Papers were coded into three main categories. The results show that the majority of the papers are concerned with disclosure themes. Considerably less salient are papers that fall under process-oriented themes and the outcomes/consequences of CSR communications. The most important outlets for CSR communication-related topics are Journal of Business Ethics and Corporate Communications: An International Journal.Originality/value – This paper represents the first attempt to perform a systematic and comprehensive overview of CSR communication papers in scholarly journals. Its value is in making this rather vast and heterogeneous literature more visible and accessible to all CSR communication scholars.Keywords - CSR communication, Scholarly journals, Systematic review, Content analysis, Special issue, Journals, Social responsibilityPaper type - Research paper
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The growing use of digital media has led to a society with plenty of new opportunities for knowledge exchange, communication and entertainment, but also less desirable effects like fake news or cybercrime. Several studies, however, have shown that children are less digital literate than expected. Digital literacy has consequently become a key part within the new national educational policy plans titled Curriculum.nu and the Dutch research and policy agendas. This research project is focused on the role the game sector can play in the development of digital literacy skills of children. In concrete, we want to understand the value of the use of digital literacy related educational games in the context of primary education. Taking into consideration that the childhood process of learning takes place through playing, several studies claim that the introduction of the use of technology at a young age should be done through play. Digital games seem a good fit but are themselves also part of digital media we want young people to be literate about. Furthermore, it needs to be taken into account that digital literacy of teachers can be limited as well. The interactive, structured nature of digital games offers potential here as they are less dependent on the support and guidance of an adult, but at the same time this puts even more emphasis on sensible game design to ensure the desired outcome. The question is, then, if and how digital games are best designed to foster the development of digital literacy skills. By harnessing the potential of educational games, a consortium of knowledge and practice partners aim to show how creating theoretical and practical insights about digital literacy and game design can aid the serious games industry to contribute to the societal challenges concerning contemporary literacy demands.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
