Older Adults' Views on Social Interactions and Online Socializing Games - A Qualitative Study.

Age-related difficulties and quarantine restrictions impede the possibilities to maintain contact with one's social network. Maintaining these contacts may be supported by digital games. To develop effective and feasible digital tools to foster social interaction, we aimed to explore what older adults find important in social contact and what barriers and enablers they foresee in digital gaming interventions as network support aids. Two focus groups and 20 semi-structured interviews (N = 29) with older adults (aged 55-87) were held to explore the research questions. Furthermore, a questionnaire was administered (N = 29) containing measures of loneliness, frailty, and social network size. Participants found 'reciprocity', 'in-person contact', and 'personal connection' important in contact with strong ties. Online games were not used much for socializing but may be used in the future, particularly by less mobile older adults. Future social gaming interventions should be challenging, user-friendly, and offer the possibility to communicate. Digital co-designed interventions that are feasible, challenging, intuitive, and trigger meaningful communication may strengthen social interactions in older adults. They may be a relevant social support tool in periods of interaction limitations due to functional impairment or social isolation.

A Global Sharing Mechanism of Resources: Modeling a Crucial Step in the Fight against Pandemics.

To face crises like the COVID-19 pandemic, resources such as personal protection equipment (PPE) are needed to reduce the infection rate and protect those in close contact with patients. The increasing demand for those products can, together with pandemic-related disruptions in the global supply chain, induce major local resource scarcities. During the first phase of the COVID-19 pandemic, we witnessed a reflex of 'our people first' in many regions. In this paper, however, we show that a cooperative sharing mechanism can substantially improve the ability to face epidemics. We present a stylized model in which communities share their resources such that each can receive them whenever a local epidemic flares up. Our main finding is that cooperative sharing can prevent local resource exhaustion and reduce the total number of infected cases. Crucially, beneficial effects of sharing are found for a large range of possible community sizes and cooperation combinations, not only for small communities being helped by large communities. Furthermore, we show that the success of sharing resources heavily depends on having a sufficiently long delay between the onsets of epidemics in different communities. These results thus urge for the pairing of a global sharing mechanism with measures to slow down the spread of infections from one community to the other. Our work uses a stylized model to convey an important and clear message to a broad public, advocating that cooperative sharing strategies in international resource crises are the most beneficial strategy for all. It stresses essential underlying principles of and contributes to designing a resilient global supply chain mechanism able to deal with future pandemics by design, rather than being subjected to the coincidental and unequal distribution of opportunities per community that we see at present.

Refining the causal loop diagram: A tutorial for maximizing the contribution of domain expertise in computational system dynamics modeling.

Complexity science and systems thinking are increasingly recognized as relevant paradigms for studying systems where biology, psychology, and socioenvironmental factors interact. The application of systems thinking, however, often stops at developing a conceptual model that visualizes the mapping of causal links within a system, e.g., a causal loop diagram (CLD). While this is an important contribution in itself, it is imperative to subsequently formulate a computable version of a CLD in order to interpret the dynamics of the modeled system and simulate "what if" scenarios. We propose to realize this by deriving knowledge from experts' mental models in biopsychosocial domains. This article first describes the steps required for capturing expert knowledge in a CLD such that it may result in a computational system dynamics model (SDM). For this purpose, we introduce several annotations to the CLD that facilitate this intended conversion. This annotated CLD (aCLD) includes sources of evidence, intermediary variables, functional forms of causal links, and the distinction between uncertain and known-to-be-absent causal links. We propose an algorithm for developing an aCLD that includes these annotations. We then describe how to formulate an SDM based on the aCLD. The described steps for this conversion help identify, quantify, and potentially reduce sources of uncertainty and obtain confidence in the results of the SDM's simulations. We utilize a running example that illustrates each step of this conversion process. The systematic approach described in this article facilitates and advances the application of computational science methods to biopsychosocial systems. (PsycInfo Database Record (c) 2023 APA, all rights reserved).