Recent evolution in the field of data science has revealed the potential utility of machine learning (ML) applied to criminal justice. Hence, the literature focused on finding better techniques to predict criminal recidivism risk is rapidly flourishing. However, it is difficult to make a state of the art for the application of ML in recidivism prediction. In this systematic review, out of 79 studies from Scopus and PubMed online databases we selected, 12 studies that guarantee the replicability of the models across different datasets and their applicability to recidivism prediction. The different datasets and ML techniques used in each of the 12 studies have been compared using the two selected metrics. This study shows how each method applied achieves good performance, with an average score of 0.81 for ACC and 0.74 for AUC. This systematic review highlights key points that could allow criminal justice professionals to routinely exploit predictions of recidivism risk based on ML techniques. These include the presence of performance metrics, the use of transparent algorithms or explainable artificial intelligence (XAI) techniques, as well as the high quality of input data.

In the professional realm, the healthcare sector stands out as one of the most susceptible to violence. One notable manifestation of this is violence among colleagues, commonly referred to as horizontal violence, which has garnered significant attention in recent times. To delve deeper into this phenomenon across various categories of healthcare professionals, a comprehensive search was conducted on PubMed, Scopus, and CINAHL, resulting in the inclusion of 13 articles. The findings of this narrative review illuminate how horizontal violence can manifest in both physical and psychological forms and how it often becomes normalized among the healthcare professionals who endure it. Particularly vulnerable to such occurrences are recent graduates and those with limited professional experience. Furthermore, it has been observed that horizontal violence has detrimental effects on the well-being of those subjected to it, as well as on the quality of patient care delivered. Considering preventive measures, numerous studies emphasize the pivotal role of effective departmental leadership in fostering a harmonious work environment. Despite the largely underreported nature of this phenomenon, the conclusions drawn in this study advocate for a deeper exploration of the dynamics surrounding horizontal violence, with the goal of devising targeted strategies to mitigate its occurrence.

Background: In the prison environment, the nursing profession has particularly complex peculiarities and aspects, so much so that prison nurses require advanced specialist skills and specific education. Can nurses’ stereotypes and prejudices in prison settings affect nursing care? What are nurses’ perceptions of the prison environment and people in detention? This study aims, on one hand, to outline the figure of the nurse in the prison environment and current regulations and, on the other hand, to explore whether and how stereotypes and prejudices may affect the way care is provided. Methods: Starting with an analysis of the literature, the authors administered a questionnaire to a group of nurses who shared data and reflections. Results: This study sheds a new light on nursing in the prison environment, exploring how nurses’ stereotypes and prejudices may affect the care of patients. Conclusions: It would be desirable to develop research in this field to enable a more conscious approach to a world that is still considered distant and dangerous, and to overcome the misperceptions and prejudices that may negatively affect the way of caring.

The rapid advancements in healthcare technologies are reshaping the medical landscape, prompting a reconsideration of clinical methodologies and their ethical foundations. This article explores the need for an updated approach to medical deontology, emphasizing the transition from traditional practices to a hybrid clinical methodology that integrates both human expertise and technological innovations. With the increasing use of Artificial Intelligence, data analytics, and advanced medical tools, healthcare professionals are presented with new ethical and professional challenges. These challenges demand a reevaluation of professional responsibility, highlighting the importance of scientific evidence in decision-making while mitigating the influence of economic and ideological factors. By framing medical practice within a systemic and integrated perspective, this article proposes a model that moves beyond the reductionist and anti-reductionist dualism, fostering a more realistic understanding of healthcare. This new paradigm necessitates the evolution of the Medical Code of Ethics, integrating the concept of “medical intelligence” to address the complexities of data management and its ethical implications. The article ultimately advocates for a dynamic and adaptive approach that aligns medical practice with emerging technologies, ensuring that patient care remains person-centered and ethically grounded in a rapidly changing healthcare environment.

Background: Undercover police work is a psychologically high-risk profession that exposes officers to chronic stress, identity conflicts, and moral dilemmas. The aim of the present review is to evaluate the psychological consequences associated with undercover police work, focusing on specific psychopathological risk factors. Methods: A systematic search was conducted in PubMed, PsycINFO, Web of Science, and Scopus databases. Studies were conducted in the United States, the United Kingdom, New Zealand, and Canada. The present systematic review analyzed data from 380 current undercover operatives, 372 former UCOs, 578 officers without undercover experience, and 60 pre-operational agents. Results: From an initial pool of 365 records, 10 studies were identified, of which 6 met the inclusion criteria. The most frequently reported psychological risk factors included anxiety, hypervigilance, identity issues, dissociative symptoms, and substance misuse. These were assessed using validated self-report instruments (e.g., SCL-90), structured interviews, and clinical evaluations. Long-term consequences were more prominent post-deployment, particularly among former UCOs. Conclusions: Undercover work is associated with an elevated risk of mental health problems, especially after the end of operations. Future research should focus on standardizing assessment tools and identifying protective factors. The findings support the development of targeted interventions such as pre-deployment psychological screening, ongoing monitoring, and structured reintegration programs to safeguard UCOs’ well-being.