The health and wellness of an individual are reliant on the integrated effects of mind, body, and spirit. This triad is intricately set within a backdrop of the environment, our earth. Western cultures often disregard this holism, especially this fourth component, in its considerations of wellness as described by modern medicine. This practice is unlike that of many of the traditional cultures in the world. These cultures focus more on balance in the context of environmental respect. Varied cultures share remarkable similarities in their healing modalities, especially considering the relative isolation from one to another-evidence that there is truth to the healing knowledge they possess. We are not disconnected from the natural world in terms of health, but dependent and interconnected within ourselves and to everything around us. Social change is required to assure that the practice of modern medicine evolves to incorporate this integral aspect of health and wellness, and this can be done through partnerships with traditional healers.There is a growing demand for wellness and earthly responsibility. It is time to appropriately learn from ageold societies and their healing traditions for they do have answers we are seeking in sustainability and harmony, environmental stewardship and planetary respect, and holistic health. For thousands of years, our ancestors have known the secrets of long life-this knowledge needs to be preserved through the apprenticeship of future generations. We propose a collaboration that develops mutually beneficial learning partnerships combining modern medical knowledge with the wisdom of traditional healers around the world.

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Data science is merging of several techniques that include statistics, computer programming, hacking skills, and a solid expertise in specific fields, among others. This approach represents opportunities for social work research and intervention. Thus, practitioners can take advantage of data science methods and reach new standards for quality performances at different practice levels. This article addresses key terms of data science as a new set of methodologies, tools, and technologies, and discusses machine learning techniques in order to identify new skills and methodologies to support social work interventions and evidence-based practice. The challenge related to data sciences application on social work practice is the shift on the focus of interventions. Data science supports data-driven decisions to predict social issues, rather than providing an understanding of reasons for social problems. This can be both a limitation and an opportunity depending on context and needs of users and professionals.

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Across human history, civilizations have responded to disasters and outbreaks of disease with increasingly complex, systematic approaches as a means of organizing chaos and protecting human life. The SARS-CoV-2 coronavirus (COVID-19) pandemic provides a unique opportunity to learn from the practice of disaster management and crisis-driven changes to patient care processes in hospital and emergent care environments worldwide. COVID-19 acts as an accelerant for process change and the need for redesign in systems where classical, linear evaluation methods most often inform carefully implemented service improvements. Strikingly, many innovative approaches and valuable lessons come from all over the globe where technology and access to resources have been most limited. This article answers the question, what can we learn about how to respond to future disasters from the evolution of disaster management as performed by helping professionals and policymakers during the past hundred-plus years and best practices seen today?Macro practitioners have co-created unique approaches within several global communities to help cope with COVID-19 and other disasters despite limited resources and seemingly unlimited needs. Referencing existing case studies of patient care responses during COVID-19 in Italy, Nigeria, South Africa, South Korea, and the United States, the authors document innovative practices and use of diverse technologies in local patient care systems. The article concludes by suggesting best practices for designing more robust, adaptive, and crisis ready responses to patient care, as well as the use of developmental evaluation as an agile approach to evaluating and improving patient services. It also suggests roles that helping professionals can play in the translation of big data systems of disaster management from organizations such as the Center for Disease Control, World Health Organization, non-governmental organizations (NGOs), and selected think tanks, among others.