Intimate partner violence is a pervasive issue deeply affecting public health, and its escalation during the COVID-19 pandemic has raised serious concerns. While the escalating impact of intimate partner violence during the COVID-19 pandemic has been widely acknowledged, there remains a need for a comprehensive systematic review that synthesizes existing literature. This review seeks to address this gap by providing an inclusive assessment of the global landscape of intimate partner violence during and after the pandemic, thereby informing more effective prevention and intervention strategies. A systematic literature search was conducted on PubMed, Google Scholar, and Scopus databases using different MeSH terms. A total of 445 relevant articles were identified initially, and after thorough screening, 54 articles were included in the review. The lockdown had several negative consequences, including job losses, economic vulnerability, and health issues due to prolonged loneliness and uncertainty. An increase in emergency hotline or Women’s Helpline calls was observed. Globally, intimate partner violence surged during the lockdown and persisted into 2023, causing severe and lasting health, psychological, and reproductive consequences for victims. Our results showed that COVID-19 increased the risk of partner violence: post-COVID intimate partner violence risk greater than pre-COVID risk (0.33 vs. 0.28, respectively). Although COVID-19 increased the risk of intimate partner violence, this review also stresses a high global prevalence of intimate partner violence, not restricted to the pandemic and lockdowns. To prevent partner violence and reduce long-lasting severe health, psychological, and reproductive consequences of partner violence, broad cooperation between governments, communities, health professionals, and the media is necessary. Intimate partner violence is a hidden pandemic that threatens women during COVID-19. The prevalence of intimate partner violence increased significantly during and after COVID-19. Governments and NGOs should adopt comprehensive protective strategies to ensure women’s safety.
As generative artificial intelligence (GenAI) shifts from novelty to necessity, its role in organizations is rapidly evolving from passive tool to influential actor. This research reimagines GenAI not as a background enabler but as a strategic participant that reshapes workflows, decision-making, and value creation in real time. Blending Actor-Network Theory (ANT) with the Business Model Canvas (BMC), the study explores how GenAI reorganizes human-machine dynamics and rewires the architecture of modern business models. Using a qualitative inductive design, we collected rich, lived insights from professionals integrating GenAI into their daily work. Novel methods, ChatGPT-assisted journaling and WhatsApp voice diaries were deployed to capture not only tasks but emotional nuance, friction points, and strategic reflections. These tools illuminated how GenAI changes the way employees think, collaborate, and lead. Guided by the five stages of ANT’s translation process, the research traces GenAI’s trajectory through organizational life. In the stage of problematization, GenAI is positioned as a solution to cognitive overload or systemic inefficiencies. During interessement, human actors begin to reshape tasks, workflows, and routines to align with the capabilities of AI tools. Enrolment follows, marking GenAI’s acceptance as a knowledge partner and an emerging co-decision-maker in both strategic and operational contexts. Through mobilization, its presence becomes stabilized as value creation and trust in its outputs grow. Finally, dissent emerges, revealing tensions around issues such as data ethics, algorithmic bias, and concerns over diminished human agency prompting a renegotiation of GenAI’s role within the organization. Meanwhile, the BMC lens reveals transformations in core business logic: GenAI redefines value propositions through content personalization, alters cost structures via automation, and reconfigures customer interaction through AI-driven dialogue. More than enhancing efficiency, GenAI drives a strategic reorientation turning knowledge creation, not just execution, into a competitive edge. The findings challenge conventional AI narratives. GenAI is not merely automating decisions; it is co-authoring organizational behavior. It fosters a cognitive shift from memorizing data to synthesizing insight and repositions learning as a strategic engine for growth. For leaders, this means that GenAI should not be managed as a tool, but engaged as a collaborator.
During menopause, women experience a range of physiological changes, including reduction in skeletal muscle mass, bone mineral density, and an increase in fat mass. Although strength training and dietary strategies have individually been shown to counteract these changes, evidence for their combined effects is currently lacking. This review aims to investigate the combinatory effects on body composition, muscle strength, and bone mineral density. Three databases (PUBMED, Web of Science, and SPORTDiscus) were screened following the PRISMA guidelines. The PEDro scale was utilized to evaluate methodological quality and potential bias risk. The analyzed outcome parameters were body composition, muscle strength, and bone mineral density. A total of 34 studies including postmenopausal women (N = 1,541) were identified; 31 of these had a PEDro score of 6 or higher. In general, body composition, muscle strength, and bone mineral density have been significantly altered through systematic strength training. Eleven studies focused on an additional calorie deficit (250-750 kcal/day) which enhanced the reduction of fat mass. Protein intake was examined in nine studies and has no significant additional effect on muscle strength and lean body mass with a minimal intake of 0.8 g/kg bodyweight. Only a few studies could be identified on other nutritional and supplementation strategies. A total of three studies were identified investigating strength training in conjunction with amino acid supplementation, four studies examining calcium and vitamin D, four studies on creatine, one study on zataria multiflora, one study on omega-3 supplementation and one study on shatavari. Systematic strength training has been consistently demonstrated to improve body composition, strength capacity, and bone mineral density. However, the evidence supporting the effectiveness of additional nutritional and supplementation strategies remains inconclusive. While a calorie-restricted diet and adequate protein intake appear to promote favourable changes in body composition, the available data is still insufficient to derive specific and evidence-based recommendations regarding supplementation in conjunction with strength training. Moreover, research on additional nutritional and supplementation strategies remains inconsistent or scarce, underscoring the need for further studies to allow for more precise recommendations. PROSPERO Registration Number CRD42023412915 (12th April 2023).
The present study investigates the psychological and behavioral implications of integrating AI into debt collection practices using data from eleven European countries. Drawing on a large-scale experimental design (n = 3514) comparing human versus AI-mediated communication, we examine effects on consumers' social preferences (fairness, trust, reciprocity, efficiency) and social emotions (stigma, empathy). Participants perceive human interactions as more fair and more likely to elicit reciprocity, while AI-mediated communication is viewed as more efficient; no differences emerge in trust. Human contact elicits greater empathy, but also stronger feelings of stigma. Exploratory analyses reveal notable variation between gender, age groups, and cultural contexts. In general, the findings suggest that AI-mediated communication can improve efficiency and reduce stigma without diminishing trust, but should be used carefully in situations that require high empathy or increased sensitivity to fairness. The study advances our understanding of how AI influences the psychological dynamics in sensitive financial interactions and informs the design of communication strategies that balance technological effectiveness with interpersonal awareness.
Background: In spite of interest in the use of artificial intelligence (AI) in nursing education, there are no studies on teaching nursing students' basics of AI technology. Innovation: An elective course for undergraduate students of nursing (final year), consigning of 10 hours of lectures, covering the following topics: Basics of AI; Principles and concepts of machine learning; Evaluation of AI-based tools; and Implementation of AI in healthcare. Results: Nineteen students attended the course and thirteen provided their feedback. Students' evaluation was positive: 77% (10 out of 13) would pick the same elective again and recommend it to others. They suggested more live demos and dedicating more time to ChatGPT. Implications: Our experience with a course focusing on basic principles of AI technology as well as list of resources and feedback of our students can be of use to nursing educators when planning similar courses. Conclusions: Since nursing students' readiness to embrace AI technology depends on understanding of the technology, educating students on foundational algorithmic principles may have a positive impact on adoption of AI in nursing practice. (c) 2025 Organization for Associate Degree Nursing. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
