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Using photo elicitation to introduce a network perspective on attachment during middle childhood
(2018)
In this article, we develop a child-centered network approach to attachment during middle childhood. Following monotropic ideas, current attachment research focuses on parental attachment figures despite the expansion of the children’s social environment during middle childhood, failing to generate a comprehensive and structured overview of all individuals who ensure the children’s feeling of safety. Relying on quantitative methods, these studies are also dominated by an adult perspective, limiting the children’s contributions. While there have been theoretical drafts of attachment networks during childhood, this article constitutes the first practical implementation. Using photo elicitation interviews and participant observations, we developed an innovative assessment strategy that allows children to exhaustively identify and characterize all their attachment figures on sociostructural and functional dimensions, thus positioning the children at the center of their comprehensive attachment networks that collectively contribute to their feeling of security. We combine qualitative and quantitative data to assess the children’s own understanding of their feeling of security and to locate the individual attachment figure on context-specific social dimensions, thus making the research setting, a clan in Cameroon, an inherent part of the methodological development. The data are translated into multidimensional network diagrams to visualize the children’s perception of their attachment environment and the emerging patterns of their selection. We present an exemplary network, supplementing it with observational data to discuss the ecological validity of our approach.
Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission (EC), recently published ethics guidelines for what it terms “trustworthy” AI. These guidelines are aimed at a variety of stakeholders, especially guiding practitioners toward more ethical and more robust applications of AI. In line with efforts of the EC, AI ethics scholarship focuses increasingly on converting abstract principles into actionable recommendations. However, the interpretation, relevance, and implementation of trustworthy AI depend on the domain and the context in which the AI system is used. The main contribution of this paper is to demonstrate how to use the general AI HLEG trustworthy AI guidelines in practice in the healthcare domain. To this end, we present a best practice of assessing the use of machine learning as a supportive tool to recognize cardiac arrest in emergency calls. The AI system under assessment is currently in use in the city of Copenhagen in Denmark. The assessment is accomplished by an independent team composed of philosophers, policy makers, social scientists, technical, legal, and medical experts. By leveraging an interdisciplinary team, we aim to expose the complex trade-offs and the necessity for such thorough human review when tackling socio-technical applications of AI in healthcare. For the assessment, we use a process to assess trustworthy AI, called 1Z-Inspection® to identify specific challenges and potential ethical trade-offs when we consider AI in practice.
Scientometric results on publication trends in clinical psychology, which refer to publication type and methodology of case studies/reports, are presented. Absolute and relative frequencies of clinical case studies are identified for the segment “mental and behavioral disorders” in MEDLINE (ICD-10 Chapter V [F]) as well as for clinical psychology publications documented in PsycINFO and PSYNDEX in 40 publication years (1975-2014). Results show an increase of the absolute number of published case studies documented in MEDLINE and PsycINFO (but not in PSYNDEX), which is highly correlated with the total increase of clinical psychology publications in both databases. Relative frequencies show another picture, namely a drop of the percentage of case studies on mental and behavioral disorders in MEDLINE, and a sharp drop in PSYNDEX since the 1980s. The trend for the relative frequency of case studies within all publications on clinical psychology documented in PsycINFO is V-shaped with 6% in the 1970s, 3% in the early 1990s, and 4-5% after the millennium. Pros and cons of case studies in clinical psychology research and education are discussed. Qualitative and quantitative case study methodologies are distinguished with respect to the phases of clinical trials and observational studies in evidence-based and empirically supported psychotherapy. Subsequently, methodological constraints are balanced with specific values in clinical training, applied research, and innovative research on the symptomatology, etiology, and classification of mental disorders as well as on combined and/or integrative treatment techniques and methods.