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The Resilience of Moses Herzog:Saul Bellow and the Humanist Novel
No full textThe oeuvre of American writer Saul Bellow (1915-2005) is deeply humanist in its emphasis on humanautonomy, growth and meaning. For Bellow, humans are self-interpreting beings with agency and theability to shape their personalities. Bellow particularly emphasises human resilience: in his novels heshows us resilient individuals who make an effort to take their lives into their own hands, refusing to bepassive victims of their circumstances and misfortunes. This view of man is most impressively expressedin Bellow’s best-known novel Herzog (1964). The protagonist Moses Herzog goes through a severe crisisand almost succumbs to his misery, but eventually emerges from his difficulties as a renewed and strongerperson after a learning process of self-examination and reorientation. In this article, I discuss Bellow’shumanist ideas, the way they take shape in Herzog, and in particular the role of resilience in them
Understanding the significance of personal bonding social capital for mental well-being of first-generation labour migrants:a cross-sectional study in the Netherlands
No full textThe present study aims to contribute to the existing, yet still limited, research literature on the association between personal bonding social capital (PBSC) and mental well-being in older populations, with a specific focus on understanding this association in a population of first-generation labour migrants with a collectivistic cultural background, living in an individualistic country.A cross-sectional study was conducted with a sample of 119 Turkish first-generation labour migrants (64.7% male; age 65–87, M(SD) = 71.13(5.04) and 124 Dutch non- migrants (32.3% male, age 65–94, M(SD) = 71.9(5.32). Both samples filled out either an online or printed questionnaire measuring PBSC (PSCSE, Simons et al., 2020), and psychological, social and emotional well-being (MHF-SF, Lamers et al., 2011) and relevant demographic covariates.Regression analyses showed positive associations between PBSC and, respectively overall mental well-being and its subdimensions emotional, social and psychological well-being in both samples. Moderation analyses showed that these associations were significantly stronger for the Turkish older migrants. These findings suggest that the migrant sample relies more heavily on close-knit homogeneous social networks for socioemotional support and assistance than the non-migrants.Research on social capital and mental well-being of older migrants is limited. This study clarifies the importance of PBSC for the mental well-being of first-generation labour migrants, considering the combined challenges they face. The results provide direction for further research and the development of practical interventions to improve mental well-being of the rapidly growing and increasingly diverse older populations
Student mental wellbeing in relation to coping, social network satisfaction, and academic stressors during and after the COVID-19 pandemic:a repeated cross-sectional study (2020-2023)
No full textThe disruption of students' social and academic environments during the COVID-19 pandemic indicates potential long-term effects on student mental wellbeing. This study examines differences and associations in mental wellbeing, academic stressors, coping strategies, and social network satisfaction among full-time students during the second lockdown (2020) and two years after the final lockdown (2023) in the Netherlands. Using a repeated cross-sectional design, validated questionnaires were completed by 877 students in 2020 and 497 in 2023. Mental wellbeing was slightly lower in 2023 (M = 46.73) than in 2020 (M = 48.55), with a significant effect of time (B = -1.75, p < .001). Female students reported lower wellbeing overall (B = -2.24, p < .001). Coping strategies were more frequently used in 2023; avoidant (B = -2.51, p < .001) and emotion-focused coping (B = -1.65, p = .034) were negatively associated with wellbeing, whereas problem-focused coping was positively related (B = 3.77, p < .001), though this effect was weaker in 2023 as compared to 2020. Academic stressors remained stable, with only academic self-perceptions showing a positive relation with wellbeing (B = 3.24, p < .001). Social network satisfaction was lower in 2023 (M = 8.85 vs. 9.43) but was still significantly associated with wellbeing (B = 1.01, p < .001). These findings suggest that mental wellbeing did not recover two years after the final lockdown. Strengthening social connectedness, positive academic self-perception and promoting adaptive coping strategies may be essential for supporting wellbeing.</p
What do you do when your career script runs out?:How older workers decide whether and how to sustain their careers
No full textA new data science trajectory for analysing multiple studies:a case study in physical activity research
No full textThe analysis of complex mechanisms within population data, and within sub-populations, can be empowered by combining datasets, for example to gain more understanding of change processes of health-related behaviours. Because of the complexity of this kind of research, it is valuable to provide more specific guidelines for such analyses than given in standard data science methodologies. Thereto, we propose a generic procedure for applied data science research in which the data from multiple studies are included. Furthermore, we describe its steps and associated considerations in detail to guide other researchers. Moreover, we illustrate the application of the described steps in our proposed procedure (presented in the graphical abstract) by means of a case study, i.e., a physical activity (PA) intervention study, in which we provided new insights into PA change processes by analyzing an integrated dataset using Bayesian networks. The strengths of our proposed methodology are subsequently illustrated, by comparing this data science trajectories protocol to the classic CRISP-DM procedure. Finally, some possibilities to extend the methodology are discussed
Quantifying time-dependent flood resilience index in a densely populated urban environment in Manado, Indonesia
No full textFlooding poses significant risks, with severe physical, economic, and psychological impacts. Efforts to mitigate flood risk include improving predictability, developing flood control structures, and enhancing community resilience. Flood resilience indices (FRIs) quantify a community's characteristics regarding prevention, preparedness, response, and recovery aspects around flood events. This study uses a time-dependent FRI in nine wards in a densely populated urban environment at the flood-vulnerable riverbank communities in Singkil Subdistrict, Manado, Indonesia. The FRI considers the temporal flood event propagation, physical vulnerability, and demographics reflecting the community's resilience to flood. The time-dependent FRI is quantified and applied for three flood return periods, 2, 20, and 100 years. Flood discharge was simulated using HEC-HMS, with results input into HEC-RAS for flood scenario modeling. FRI was then assessed using HEC-RAS flood propagation results for the event phase and national socio-economic data for the flood recovery phase. The study captures the community's immediate responses and near-term adaptations, where resilience differs by location and fluctuates across the flood scenarios. Most of the wards have the characteristics of rapid FRI decrease caused by the sudden onset of the incoming flood and a more gradual recovery of the FRI. Comparison between the three return periods shows slight FRI curve differences, indicating that the relatively high probability of 2-year floods is already significantly damaging the community. These findings prompt a call for location-specific, effective and time-sensitive flood mitigation strategies, especially in densely populated vulnerable wards in urban environments.</p
An interdisciplinary e-learning intervention for professionals working with breast cancer survivors and chronic pain:a realist evaluation
No full textPurpose: Chronic pain is prevalent among breast cancer survivors. Bio-psychosocial factors interplay in its exacerbation and maintenance. Therefore, prevention and treatment require an interdisciplinary response and the integration of various approaches. To deliver this way of working, healthcare professionals may need training. We developed an e-learning intervention, aimed at increasing awareness and interdisciplinary collaboration in response to pain after cancer. We aimed to gain insight into the intervention’s implementation, mechanisms, and outcomes through a realist evaluation. Methods: A mixed-methods pre- and post-test design with follow-up was used. Via questionnaires, professionals reported on the feasibility of the e-learning and their knowledge, beliefs, confidence, and professional role in pain prevention and treatment. Six-month post-intervention, interviews were conducted to explore transferability in practice. Results: An interdisciplinary group of 22 professionals completed the intervention. Overall, e-learning was deemed a feasible format for training. An increase in confidence was found, whereas no changes were detected in knowledge and professional role. Configurations were outlined between these mechanisms and behaviors in practice, influenced by implementation processes and context-related factors. Conclusions: E-learning holds promise in stimulating knowledge, beliefs, confidence, and professional role. In this, the value of asynchronous discussion forums, case-based exercises, practical tools, and models were emphasized.</p
Improving operational decision-making through decision mining - utilizing method engineering for the creation of a decision mining method
No full textContext: This study addresses the challenge of enhancing the efficiency and agility of decision support software supporting both operational decision-making and software production teams developing decision support software. It centers on creating a method that assists in mining decisions, checking decisions on conformance, and improving decisions, which supports software production teams in developing decision support software. Objective: The primary objective is to develop an explicit, clear, and structured approach for discovering, checking, and improving decisions using decision support software. The study aims to create a blueprint for software production teams to develop Decision Mining (DM) software, in line with recent advancements in the field. Additionally, it seeks to provide a consolidated, methodical overview of activities and deliverables in the DM research field. Method: The research employs method engineering principles to construct a method for DM that leverages the existing body of knowledge by utilizing a Systematic Literature Review (SLR). The study focuses on developing individual building blocks and method fragments incorporated into seven DM scenarios. Results: The study led to the creation of a Decision Mining Method (DMM), which includes 138 method fragments grouped into eleven categories. These fragments were systematically merged to form a comprehensive DMM. The method encapsulates the complexity of DM and provides practical applicability in real-world scenarios, highlighted by the identification of seven distinct scenarios in DM phases. The study also conducted the first SLR in the DM field, providing a comprehensive overview of current practices and outcomes. Conclusion: The study helps in advancing the DM field by creating a structured approach and a comprehensive method for DM, aligning with recent developments in the field. It successfully aggregated the fragmented DM domain into a cohesive methodological overview, crucial for future research. The study also lays out a detailed agenda for future research, focusing on expanding and validating the DMM, incorporating cross-disciplinary insights, and addressing the challenges in machine learning within DM. The future research directions aim to refine and broaden the applicability of the DMM, ensuring its effectiveness in diverse practical contexts and contributing to a more holistic and comprehensive approach to decision mining.</p
Abstract Dialectical Frameworks are Boolean Networks
No full textAbstract dialectical frameworks are a unifying model of formal argumentation, where argumentative relations between arguments are represented by assigning acceptance conditions to atomic arguments. Their generality allows them to cover a number of different approaches with varying forms of representing the argumentation structure. Boolean regulatory networks are used to model the dynamics of complex biological processes, taking into account the interactions of biological compounds, such as proteins or genes. These models have proven highly useful for comprehending such biological processes, allowing to reproduce known behaviour and testing new hypotheses and predictions in silico, for example in the context of new medical treatments. While both these approaches stem from entirely different communities, it turns out that there are striking similarities in their appearence. In this paper, we study the relation between these two formalisms revealing their communalities as well as their differences, and introducing a correspondence that allows to establish novel results for the individual formalisms.</p