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Recent discussions of model selection and multimodel inference highlight a general challenge for researchers: how to convey the explanatory content of a hypothesized model or set of competing models clearly. The advice from statisticians for scientists employing multimodel inference is to develop a well-thought-out set of candidate models for comparison, though precise instructions for how to do that are typically not given. A coherent body of knowledge, which falls under the general term causal analysis, now exists for examining the explanatory scientific content of candidate models. Much of the literature on causal analysis has been recently developed, and we suspect may not be familiar to many ecologists. This body of knowledge comprises a set of graphical tools and axiomatic principles to support scientists in their endeavors to create “well-formed hypotheses,” as statisticians are asking them to do. Causal analysis is complementary to methods such as structural equation modeling, which provides the means for evaluation of proposed hypotheses against data. In this paper, we summarize and illustrate a set of principles that can guide scientists in their quest to develop explanatory hypotheses for evaluation. The principles presented in this paper have the capacity to close the communication gap between statisticians, who urge scientists to develop well-thought-out coherent models, and scientists, who would like some practical advice for exactly how to do that.

By now, personal life has been invaded by online social networks (OSNs) everywhere. They intend to move more and more offline lives to online social networks. Therefore, online social networks can reflect the structure of offline human society. A piece of information can be exchanged or diffused between individuals in social networks. From this diffusion process, lots of latent information can be mined. It can be used for market predicting, rumor controlling, and opinion monitoring among other things. However, the research of these applications depends on the diffusion models and methods. For this reason, we survey various information diffusion models from recent decades. From a research process view, we divide the diffusion models into two categories—explanatory models and predictive models—in which the former includes epidemics and influence models and the latter includes independent cascade, linear threshold, and game theory models. The purpose of this paper is to investigate the research methods and techniques, and compare them according to the above categories. The whole research structure of the information diffusion models based on our view is given. There is a discussion at the end of each section, detailing related models that are mentioned in the literature. We conclude that these two models are not independent, they always complement each other. Finally, the issues of the social networks research are discussed and summarized, and directions for future study are proposed.

Digital transformation has been one of the main trends in organizations in recent years, and digital literacy is a critical factor in the success of this transformation. Digital transformation involves the use of digital technologies to improve an organization's processes, products, and services. For this transformation to be successful, it is necessary for employees to have knowledge of and skills in digital technologies. Digital literacy allows employees to understand technologies and their applications, know how to use them efficiently and safely, evaluate and select the most appropriate digital tools for each task, and be prepared to deal with problems and challenges that arise in the digital environment. Thus, this study is relevant because it seeks to understand how digital literacy can impact Digital Transformation in organizations and, through the construction of an explanatory model, allows the identification of variables that influence this relationship by developing strategies to improve the digital literacy of employees in organizations.

Background: Forecasting the behavior of epidemic outbreaks is vital in public health. This makes it possible to anticipate the planning and organization of the health system, as well as possible restrictive or preventive measures. During the COVID-19 pandemic, this need for prediction has been crucial. This paper attempts to characterize the alternative models that were applied in the first wave of this pandemic context, trying to shed light that could help to understand them for future practical applications. Methods: A systematic literature search was performed in standardized bibliographic repertoires, using keywords and Boolean operators to refine the findings, and selecting articles according to the main PRISMA 2020 statement recommendations. Results: After identifying models used throughout the first wave of this pandemic (between March and June 2020), we begin by examining standard data-driven epidemiological models, including studies applying models such as SIR (Susceptible-Infected-Recovered), SQUIDER, SEIR, time-dependent SIR, and other alternatives. For data-driven methods, we identify experiences using autoregressive integrated moving average (ARIMA), evolutionary genetic programming machine learning, short-term memory (LSTM), and global epidemic and mobility models. Conclusions: The COVID-19 pandemic has led to intensive and evolving use of alternative infectious disease prediction models. At this point it is not easy to decide which prediction method is the best in a generic way. Moreover, although models such as the LSTM emerge as remarkably versatile and useful, the practical applicability of the alternatives depends on the specific context of the underlying variable and on the information of the target to be prioritized. In addition, the robustness of the assessment is conditioned by heterogeneity in the quality of information sources and differences in the characteristics of disease control interventions. Further comprehensive comparison of the performance of models in comparable situations, assessing their predictive validity, is needed. This will help determine the most reliable and practical methods for application in future outbreaks and eventual pandemics.

Genuinely new discovery transcends existing knowledge. Despite this, many analyses in systems neuroscience neglect to test new speculative hypotheses against benchmark empirical facts. Some of these analyses inadvertently use circular reasoning to present existing knowledge as new discovery. Here, I discuss that this problem can confound key results and estimate that it has affected more than three thousand studies in network neuroscience over the last decade. I suggest that future studies can reduce this problem by limiting the use of speculative evidence, integrating existing knowledge into benchmark models, and rigorously testing proposed discoveries against these models. I conclude with a summary of practical challenges and recommendations.

Digital transformation has been one of the main trends in organizations in recent years, and digital literacy is a critical factor in the success of this transformation. Digital transformation involves the use of digital technologies to improve an organization’s processes, products, and services. For this transformation to be successful, it is necessary for employees to have knowledge of and skills in digital technologies. Digital literacy allows employees to understand technologies and their applications, know how to use them efficiently and safely, evaluate and select the most appropriate digital tools for each task, and be prepared to deal with problems and challenges that arise in the digital environment. This study investigates the relationship between digital transformation and digital literacy through a Systematic Literature Review conducted in accordance with Kitchenham’s guidelines. A total of 54 articles, published from 2018, were analyzed from databases such as Scopus, Science Direct, IEEE and Springer. The results reveal that digital literacy significantly influences the success of digital transformation, particularly in areas such as employee adaptability, innovation capacity, and digital tool integration. Key mediating and moderating factors identified include organizational learning culture, leadership support, ongoing training programs, and technological infrastructure. Based on these findings, an explanatory model was developed that maps the interaction between these variables and their impact on digital transformation outcomes. The study offers practical implications for organizations seeking to enhance their digital maturity: investing in employee digital literacy development, aligning leadership strategies with digital initiatives, and fostering a supportive culture for digital adoption are crucial steps. Thus, this study is relevant because it seeks to understand how digital literacy can impact Digital Transformation in organizations and, through the construction of an explanatory model, allows the identification of variables that influence this relationship by developing strategies to improve the digital literacy of employees in organizations.

Background That there is a relationship between human rights and health is well established and frequently discussed. However, actions intended to take account of the relationship between human rights and social determinants of health have often been limited by lack of clarity and ambiguity concerning how these rights and determinants may interact and affect each other. It is difficult to know what to do when you do not understand how things work. As our own understanding of this consideration is founded on perspectives provided by the critical realist paradigm, we present an account of and commentary on our application of these perspectives in an investigation of this relationship. Findings We define the concept of paradigm and review critical realism and related implications for construction of knowledge concerning this relationship. Those implications include the need to theorise possible entities involved in the relationship together with their distinctive properties and consequential power to affect one another through exercise of their respective mechanisms (ways of working). This theorising work enabled us identify a complex, multi-layered assembly of entities involved in the relationship and some of the array of causal mechanisms that may be in play. These are presented in a summary framework. Conclusion Researchers’ views about the nature of knowledge and its construction inevitably influence their research aims, approaches and outcomes. We demonstrate that by attending to these views, which are founded in their paradigm positioning, researchers can make more progress in understanding the relationship between human rights and the social determinants of health, in particular when engaged in theorizing work. The same approaches could be drawn on when other significant relationships in health environments are investigated.

This study investigated explanatory models of autism among parents of young children with ASD in the multicultural context of Sweden. Seventeen parents from diverse cultural, ethnic and linguistic backgrounds participated in semi-structured interviews. A deductive approach to qualitative content analysis was used to analyze data. Five domains of the Explanatory Model supplementary module of the Cultural Formulation Interview (CFI) were used as coding categories, operationalized as 'Parents' understanding of autism'; 'Autism prototypes'; 'Causal explanations'; 'Course of autism', and 'Help seeking and treatment expectations' The results showed that parents' prior knowledge of autism and experience of young children's typical developmental trajectories, as well as the opinions of children's grandparents and preschool teachers, affected symptom recognition and help seeking. There were differences in parents' explanatory models before and after ASD diagnosis. Initial interpretations of the disorder included medical conditions and reaction to environmental influences, while genetic, supernatural/religious factors, and vaccinations were mentioned as definite causes after obtaining a clinical diagnosis. Parents also held multiple explanatory models, influenced by the views of family members and information obtained from media or from health care professionals. Parents' treatment decisions included use of available state-funded support services, and complementary and alternative treatments. The results demonstrate the utility of the CFI's Explanatory Model supplementary module in autism research. Implications for clinical practice are discussed.

Background Despite a high burden of mental health problems among refugees, there is limited knowledge about effective mental health care provision for this group. Although substantial efforts in understanding the complexity of cross-cultural psychotherapy – which in the context of this study we use to refer to therapy with client and therapist of different cultural backgrounds – have been made, there remains a dearth of research exploring barriers for effective cross-cultural psychotherapy. This study aimed at narrowing this gap in knowledge by exploring major challenges encountered by psychotherapists in cross-cultural psychotherapy and strategies which have proven useful in overcoming such challenges. Methods We employed a qualitative study design, conducting semi-structured in-depth interviews with 10 purposely selected psychotherapists working with refugees in Germany. Respondents were from varying theoretical background and had varying levels of experience. Data were analyzed using a thematic approach, following a mix of deductive and inductive coding. Results Respondents reported three main challenges in their cross-cultural practice: different or unrealistic expectations of clients towards what psychotherapy would offer them; challenges grounded in different illness explanatory models; and communication challenges. In dealing with these challenges, respondents recommended psychoeducation to overcome issues related to problematic expectations towards psychotherapy; “imagining the real”, identifying “counter magic” and other client-appropriate resources to deal with issues related to clients’ foreign illness attributions; and translators in dealing with communication barriers, though the latter not univocally. Conclusions Results show that psychotherapy with refugees can be very successful, at least from the psychotherapist perspective, but also poses significant challenges. Our findings underline the importance of developing, testing, and institutionalizing structured and structural approaches to training psychotherapists in cross-cultural therapy at scale, to accommodate the rising mental health care need of refugees as a client group.

Background 98% of people with surgical conditions living in low- and middle-income countries (LMICs) do not receive safe, timely and affordable surgical and anesthesia care. Research exploring barriers to receiving care has tended to be narrow in focus, often facility-based and ignoring the community beliefs, experiences and behaviours that will be an essential component of closing the gap in surgical care. Using qualitative methods, we captured diverse community perspectives in rural Ethiopia: exploring beliefs, perceptions, knowledge and experiences related to surgical conditions, with the overall aim of (re)constructing explanatory models. Methods Our study was nested within a community-based survey of surgical conditions conducted in the Butajira Health and Demographic Surveillance Site, southern Ethiopia, and a follow-up study of people accessing surgical care in two local hospitals. We carried out 24 semi-structured interviews. Participants were community members who needed but did/did not access surgical care, community-based healthcare workers and traditional bone-setters. Interviews were conducted in Amharic, audio-recorded, transcribed, and translated into English. We initially carried out thematic analysis and we recognized that emerging themes were aligned with Kleinman’s explanatory models framework and decided to use this to guide the final stages of analysis. Results We found that community members primarily understood surgical conditions according to severity. We identified two categories: conditions you could live with and those which required urgent care, with the latter indicating a clear and direct path to surgical care whilst the former was associated with a longer, more complex and experimental pattern of help-seeking. Fear of surgery and poverty disrupted help-seeking, whilst community narratives based on individual experiences fed into the body of knowledge people used to inform decisions about care. Conclusions We found explanatory models to be flexible, responsive to new evidence about what might work best in the context of limited community resources. Our findings have important implications for future research and policy, suggesting that community-level barriers have the potential to be responsive to carefully designed interventions which take account of local knowledge and beliefs.