The Tavistock Institute of Human Relations is a British not-for-profit organisation that applies social science to contemporary issues and problems. It was initiated in 1946, when it developed from the Tavistock Clinic, and was formally established as a separate entity in September 1947. The journal Human Relations is published on behalf of the Tavistock Institute by Sage Publications. The Institute is located in Gee Street in Clerkenwell, London.
Autonomous Sensory Meridian Response (ASMR) is a multisensory experience most often associated with feelings of relaxation and altered consciousness, elicited by stimuli which include whispering, repetitive movements, and close personal attention. Since 2015, ASMR research has grown rapidly, spanning disciplines from neuroscience to media studies but lacking a collaborative or interdisciplinary approach. To build a cohesive and connected structure for ASMR research moving forwards, a modified Delphi study was conducted with ASMR experts, practitioners, community members, and researchers from various disciplines. Ninety-eight participants provided 451 suggestions for ASMR research priorities which were condensed into 13 key areas: (1) Definition, conceptual clarification, and measurement of ASMR; (2) Origins and development of ASMR; (3) Neurophysiology of ASMR; (4) Understanding ASMR triggers; (5) Factors affecting the likelihood of experiencing/eliciting ASMR; (6) ASMR and individual/cultural differences; (7) ASMR and the senses; (8) ASMR and social intimacy; (9) Positive and negative consequences of ASMR in the general population; (10) Therapeutic applications of ASMR in clinical contexts; (11) Effects of long-term ASMR use; (12) ASMR platforms and technology; (13) ASMR community, culture, and practice. These were voted on by 70% of the initial participant pool using best/worst scaling methods. The resulting agenda provides a clear map for ASMR research to enable new and existing researchers to orient themselves towards important questions for the field and to inspire interdisciplinary collaborations.
Alongside formal agencies and specialist services, informal and social networks play a pivotal role in responding to domestic abuse in the community. Friends, relatives, and neighbours are uniquely placed to recognise domestic abuse, respond to victim-survivors, and refer to wider services. Yet, informal networks may not know how to respond and feel overwhelmed or ill-equipped to offer support. This mixed method systematic review examined the role of training for equipping and enabling informal supporters to respond to disclosures of abuse. To do so, four bibliographic databases, alongside specialist repositories and websites, were searched for empirical studies of educational programmes tailored towards informal networks. Nine included studies were subject to data extraction and quality appraisal by two members of the review team. A thematic synthesis of qualitative data was undertaken to identify the characteristics of training deemed important by informal supporters. The findings focused on engaging informal supporters in the issue of domestic abuse, training content, materials, and delivery. The themes were translated into ten recommendations and juxtaposed with studies reporting quantitative evidence of effectiveness. The resulting matrix was used to identify five characteristics of successful training for informal supporters. These included training content that covered (a) warning signs and nature of domestic abuse, (b) impacts of abuse, and (c) how to respond/local resources. Tailoring training to the context/audience and the use of nontext formats were also evidenced by included studies. The review highlights the potential of educational programmes for enabling community responses to victim-survivors. Such interventions offer opportunities for formal agencies to collaborate with informal networks and strengthen the societal-wide response to domestic abuse.
This article outlines recent developments in AI from various positive and negative perspectives, examining its impact on the weather, on warfare, in the workplace, in the classroom, and in agriculture. It explores how such developments will impact employment by using AI to replace workers and professionals and outlines how those developments are already having an impact particularly on call centres. AI, with its capacity to create fake news and fake people, is likely to impact on communication and on relationships. This will lead to a loss of trust for key figures through the capacity of AI to be used malignly as a means of punishing, manipulating, or controlling others. The blurring of the boundary between illusion and reality and the replacement of individuals with robotics and bots in the workplace will affect the population's relationship with employers, politicians, and other powerful figures. This article considers the likely societal and global threat on populations in terms of social cohesion and explores whether the introduction of Universal Basic Income, which is being trialled extensively in various countries, would be one way of providing the stability that loss of employment will bring.
Theory of Change diagrams are commonly used within evaluation. Due to their popularity and flexibility, Theories of Change can vary greatly, from the nuanced and nested, through to simplified and linear. We present a methodology for building genuinely holistic, complexity-appropriate, system-based Theory of Change diagrams, using Participatory Systems Mapping as a starting point. Participatory System Maps provide a general-purpose resource that can be used in many ways; however, knowing how to turn their complex view of a system into something actionable for evaluation purposes is difficult. The methodology outlined in this article gives this starting point and plots a path through from systems mapping to a Theory of Change evaluators can use. It allows evaluators to develop practical Theories of Change that take into account feedbacks, wider context and potential negative or unexpected outcomes. We use the example of the energy trilemma map presented elsewhere in this special issue to demonstrate.
