Search Results (3)

In public security policing, where pressure is constant, effective decision-making and emotion regulation are critical, especially for leaders. These processes significantly impact upon work results, performance, officials’ health, employee well-being, and the organizational environment. This study aims to broaden the understanding of decision-making styles and emotion-regulation strategies used by police officials in the Portuguese Public Security Police (PSP). We surveyed 138 Portuguese high-ranking police officials using two self-response questionnaires, namely the Emotion Regulation in the Workplace (ReTrab) and the Melbourne Decision Making Questionnaire (MDMQ), both translated, adapted, and validated for the Portuguese police context. A confirmatory factor analysis was conducted, followed by correlations to explore the relationship between emotion-regulation strategies and decision-making styles. Finally, through a regression analysis, the potential impact of this relationship was assessed. The results reveal that specific emotion-regulation strategies, except for adaptive ones, significantly influence and modify the decision-making styles of PSP officials. Dysfunctional and maladaptive emotion-regulation strategies lead to less adaptive decision-making styles, while functional strategies promote more adaptive styles. These findings have theoretical and practical implications, offering valuable insights for targeted training programs and interventions in the law-enforcement sector, benefiting the police personnel, the communities they serve, and public perceptions about police. Full article

Criminal investigations collect and analyze the facts related to a crime, from which the investigators can deduce evidence to be used in court. It is a multidisciplinary and applied science, which includes interviews, interrogations, evidence collection, preservation of the chain of custody, and other methods and techniques of investigation. These techniques produce both digital and paper documents that have to be carefully analyzed to identify correlations and interactions among suspects, places, license plates, and other entities that are mentioned in the investigation. The computerized processing of these documents is a helping hand to the criminal investigation, as it allows the automatic identification of entities and their relations, being some of which difficult to identify manually. There exists a wide set of dedicated tools, but they have a major limitation: they are unable to process criminal reports in the Portuguese language, as an annotated corpus for that purpose does not exist. This paper presents an annotated corpus, composed of a collection of anonymized crime-related documents, which were extracted from official and open sources. The dataset was produced as the result of an exploratory initiative to collect crime-related data from websites and conditioned-access police reports. The dataset was evaluated and a mean precision of $0.808$, recall of $0.722$, and F1-score of $0.733$ were obtained with the classification of the annotated named-entities present in the crime-related documents. This corpus can be employed to benchmark Machine Learning (ML) and Natural Language Processing (NLP) methods and tools to detect and correlate entities in the documents. Some examples are sentence detection, named-entity recognition, and identification of terms related to the criminal domain. Full article

Several studies have shown that European police crash reports provide different detail degrees of work zone crash-related data. In this sense, the present study aims to verify the possibility of identifying significant risk factors involved in the occurrence of road work zone crashes with casualties, based on the official data usually available, through a descriptive, binary logistic, and probit regression statistical analysis. To accomplish the analysis, a total of 2597 police-reports related to 1767 Portuguese work zone crashes that occurred during the 2013–2015 period were considered and binary logistic and probit regression models were estimated by the main type of crash, contributing factor, and driver age group. Fifteen explanatory variables, selected based on the literature review and crash data provided in police crash reports, were considered in the analysis. The results obtained for the estimated coefficients and goodness-of-fit test values were found very similar for both link functions (logit and probit) and it was possible to identify risk factors. The modeling results pointed to excessive speed, disregard for vertical signs, luminosity, intersections, and motorcycle and heavy vehicle involvement as the most significant risk factors. Given the results, it is possible to conclude that binary logistic regression can be used in the statistical analysis of the available police official work zone crash data to identify and get some insight into the risk factors involved in work zone crashes. Data analysis also revealed the need to promote adequate and complete crash report filling by police officers. While police crash reports are not revised and standardized to incorporate more detailed work zone crash information, this approach can be used to support a more efficient road operation decision making and the review of some aspects related to work zone layout design. Full article