From social workers to socio-therapists: the transformative journey of substance abuse therapists

ABSTRACT

The efficiency of interventions in addiction is associated with the processes of practitioners’ professional development. Identity formation is studied extensively for clinical professions but little research focuses on social workers working in addiction settings. This study examined the experience of social workers working in a community treatment network in Greece. Fifteen interviews were conducted with 11 women and 4 men, ranging between 29 and 47 years οf age, who worked in different centers within the network. Data were analyzed following the guidelines of Interpretative Phenomenological Analysis, offering in-depth examinations of people’s lived experience. The results delineate a series of transformations in professional identity comprising a process of biographization. The participants highlighted intrapersonal and relational factors that influence this process which leads to self-understanding and engagement with the therapeutic community. The results contribute to deciphering the factors that influence social workers’ experience in their professional identity formation in substance abuse treatment contexts.     

Real-time threat assessment based on hidden Markov models

An essential factor toward ensuring the security of individuals and critical infrastructures is the timely detection of potentially threatening situations. To this end, especially in the law enforcement context, the availability of effective and efficient threat assessment mechanisms for identifying and eventually preventing crime- and terrorism-related threatening situations is of utmost importance. Toward this direction, this work proposes a hidden Markov model-based threat assessment framework for effectively and efficiently assessing threats in specific situations, such as public events. Specifically, a probabilistic approach is adopted to estimate the threat level of a situation at each point in time. The proposed approach also permits the reflection of the dynamic evolution of a threat over time by considering that the estimation of the threat level at a given time is affected by past observations. This estimation of the dynamic evolution of the threat is very useful, since it can support the decisions by security personnel regarding the taking of precautionary measures in case the threat level seems to adopt an upward trajectory, even before it reaches the highest level. In addition, its probabilistic basis allows for taking into account noisy data. The applicability of the proposed framework is showcased in a use case that focuses on the identification of potential threats in public events on the basis of evidence obtained from the automatic visual analysis of the footage of surveillance cameras.