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Bayesian Network Analysis of Intervention-Induced Physical Activity Behavior Change:Comparative Modeling Study Across Age, Education, and Activity Impairment Subgroups
No full textBackground:Tailoring intervention content, such as those designed to improve physical activity (PA) behavior, can enhance effectiveness. Previous Bayesian network research showed that it might be relevant to tailor PA interventions based on demographic factors such as gender, revealing differences in determinants’ roles between subpopulations. In order to optimize tailoring, one needs to understand the differences between subpopulations based on different characteristics. Building on this, this study examines age, education level, and PA impairment as key moderators, as these factors might influence PA engagement and intervention responsiveness. Older adults, for example, rely more on habitual behavior, lower-educated individuals may face challenges due to lower health literacy and socioeconomic inequalities, and individuals with PA impairment, defined as functional impairments restricting PA, may face unique barriers to PA. Understanding differences based on these factors is crucial for optimizing interventions and ensuring effectiveness across diverse populations.Objective:This study investigates, by means of Bayesian networks, differences in PA intervention mechanisms of subpopulations based on age, education level, and PA impairment.Methods:Subpopulation-specific subsets from an integrated dataset of 5 studies are analyzed, including demographics, experimental group assignment, and PA and sociocognitive measures at baseline, short term, and long term. The relevant subpopulations are defined based on age, education level, and PA impairment. For each subpopulation, a stable Bayesian network is estimated based on the corresponding subset of data by applying a bootstrap procedure and according to a confidence threshold, relevant paths of the model are visualized in order to find indications regarding subpopulation-specific intervention mechanisms.Results:A comparison of subpopulation-specific models unveils similarities and differences with respect to determinants’ roles in PA behavior change induced by interventions. Similar structures of determinants affect short-term PA, ultimately causing effects in the long term, where intention and habit are directly related to PA for most subpopulations. With respect to age-based differences, the interventions influence PA less via attitude cons and planning for older than younger people. Looking at the level of education, planning and intrinsic motivation are less influential for low-educated participants compared with high- or medium-educated participants, whereas more influence takes place through attitude pros for this low-educated group with respect to maintaining effects in the long term. Looking at PA impairments, apart from the findings that attitude pros and planning are more relevant in the pathway of change for people without impairment, a more interesting insight is that fewer determinants are directly influenced by the intervention within the group with PA impairment.Conclusions:Intervention mechanisms in specific demographic groups have been rarely studied so far. Initial interpretations from the derived subpopulation models in this study unveil subpopulation-specific patterns of behavioral change, which enable better tailoring of intervention content to characteristics of the target population in order to induce or enhance effects
An Organizational-Level Intervention to Enhance Sustainable Employability of Long-Term Care Workers:A Process Evaluation of the Healthy Working Approach
No full textOBJECTIVES: The aims of the study were to evaluate whether the Healthy Working Approach was implemented as planned and to gain insight into perceived positive outcomes and barriers and facilitators to the implementation.METHODS: We conducted a mixed methods process evaluation on a team-based approach for sustainable employability of long-term care workers. We examined context, recruitment, reach, dose delivered, dose received, fidelity, satisfaction, perceived positive outcomes, and barriers and facilitators.RESULTS: Reach was 11% whereas dose delivered, dose received, fidelity, and satisfaction were above 60%. Perceived positive outcomes included improved team coherence, faster problem solving, and professional development. Barriers and facilitators included collaboration in working groups, motivation, and scheduling meetings.CONCLUSIONS: For the teams reached, the approach was implemented according to protocol and perceived as positive. Future implementation should focus on increasing reach and dose delivered from working groups to teams.</p
Time-domain reconstruction of signals and glitches in gravitational wave data with deep learning
No full textGravitational wave (GW) detectors, such as LIGO, Virgo, and KAGRA, detect faint signals from distant astrophysical events. However, their high sensitivity also makes them susceptible to background noise, which can obscure these signals. This noise often includes transient artifacts called “glitches” that can mimic genuine astrophysical signals or mask their true characteristics. In this study, we present DeepExtractor, a deep learning framework that is designed to reconstruct signals and glitches with power exceeding interferometer noise, regardless of their source. We design DeepExtractor to model the inherent noise distribution of GW detectors, following conventional assumptions that the noise is Gaussian and stationary over short timescales. It operates by predicting and subtracting the noise component of the data, retaining only the clean reconstruction of the signal or glitch. We focus on applications related to glitches and validate DeepExtractor’s effectiveness through three experiments: (1) reconstructing simulated glitches injected into simulated detector noise, (2) comparing its performance with the state-of-the-art BayesWave algorithm, and (3) analyzing real data from the Gravity Spy dataset to demonstrate effective glitch subtraction from LIGO strain data. We further demonstrate its potential by reconstructing three real GW events from LIGO’s third observing run, without being trained on GW waveforms. Our proposed model achieves a median mismatch of only 0.9% for simulated glitches, outperforming several deep learning baselines. Additionally, DeepExtractor surpasses BayesWave in glitch recovery, offering a dramatic computational speedup by reconstructing one glitch sample in approximately 0.1 s on a CPU, compared to BayesWave’s processing time of approximately one hour per glitch
De advocatuur onder druk
No full textIn Nederland staat de advocatuur de laatste tijd weer volop in de belangstelling. Veelal gaat het om het feit dat de sociale advocatuur onder druk staat: het aanbod van sociaal advocaten neemt af, de overheidsvergoedingen zijn te laag en er is onvoldoende landelijke dekking. Ook zijn er de laatste jaren steeds meer zorgen over de druk die op advocaten wordt uitgeoefend in de vorm van agressie, bedreiging, intimidatie of zelfs gedwongen verdwijningen en moord.1Recent is ook politieke druk op advocaten aan dit niet zo’n fraaie lijstje toegevoegd. President Donald Trump bemoeit zich namelijk steeds intensiever met de advocatuur in de Verenigde Staten van Amerika (hierna: VS). In deze bijdrage wordt besproken dat deze bemoeienissen ook directe gevolgen kunnen hebben voor Nederland
Only practical knowledge or knowing the algorithm?:Notions and necessities of explainable artificial intelligence in long-term care
No full textThe number of older adults living independently at home is expanding, which is often said to bring the need for more technological assistance. Dutch policy aims to allow older adults to remain living at home as long as possible. In such policies, the use of technologies supports older adults to perform daily practices. Artificial intelligence (AI), as part of these technologies, has the potential to improve personalized care and ageing in place, both at home and in residential care settings. However, the internal machineries of AI systems often remain hidden as a black box for the users, which can include caregivers or older adults. Interest in explainable AI (XAI) originates from this ‘black boxing’, as a technique to assist users in understanding the underlying logic of the decision-making process, and in identifying mistakes, transforming the opaque black box into an interpretable twin ‘white box’. Current research is mostly located in the technical domains, and it remains unknown how various stakeholders see XAI. To fill this gap, we conducted 21 scenario-based interviews with professionals to investigated XAI in three long-term care contexts: company, care management and care provision. We draw on the theory of the co-constitution of ageing and AI to reach our aim of understanding ‘what is XAI’ in the different contexts, and the enactments of XAI in their practices. Participants express different notions and necessities of XAI, varying from knowing algorithms towards practical understanding. The needed level of explainability is divers in the different contexts of care. As a follow-up, we recommend to include older adults and perform research into the enactment of XAI in practice, and the form or degree of XAI needed and for whom
Artificial Intelligence as an Enabler of Dynamic Capabilities:A ‘Sense-Shape-Shift’ Perspective on Digital Transformation During Disruption
No full textOrganizations facing crises must rapidly adapt and embrace digital transformation to ensure continuity and competitiveness. This study examines how AI capability and organizational learning mechanisms support the development of dynamic capabilities—sensing, shaping, and shifting—and how these drive digital transformation and performance under disruption. Based on survey data from 257 executives and managers, a composite-based structural equation modeling approach was applied. The results show that AI capability significantly enhances dynamic capabilities, while learning mechanisms are essential for their development. Among these, shifting capability plays a critical role in enabling digital transformation and improving competitive performance during crises. These findings provide actionable insights for organizations aiming to strengthen resilience and adaptability by leveraging AI and learning. Dynamic capabilities, supported by technological and organizational enablers, are key to navigating disruption and sustaining advantage in volatile environments
Financial literacy programs in school settings: A scoping review
No full textFinancial literacy programs in schools aim to equip students with the knowledge and skills needed for sound financial decision-making. This review provides an integrated overview of program content, outcome measures, and assessment methods. A systematic search of seven databases identified 56 relevant studies. The review highlights substantial heterogeneity in how financial literacy is conceptualized and operationalized, which limits comparability across studies. While most studies report positive effects, variation in methodological quality raises concerns about the robustness of these findings. By mapping these patterns, the review establishes a foundation for more coherent, evidence-based guidance for policymakers and educators seeking to design and evaluate effective financial literacy initiatives
