The probationer–probation officer working alliance plays an important role in the outcome of probation supervision. This study explored the development of the working alliance between probationers and probation officers in the Netherlands, from the perspective of both probationers and probation officers. More specifically, we explored the significance of different aspects of the working alliance at the start of probation supervision and after a three-month period, as well as the role played by critical incidents during the supervisory process and their subsequent effect on the working alliance. Overall, the study showed that clarity over goals and restrictions was initially the most salient issue for both parties, and that after a three-month period the working alliance evolved into a trusting relationship. Several incidents were identified, probationers identified more positive moments and less negative moments than their PO counterparts. If these types of incidents are managed accordingly by the probation officer, then they can ultimately serve to strengthen the relationship.
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In case of a major cyber incident, organizations usually rely on external providers of Cyber Incident Response (CIR) services. CIR consultants operate in a dynamic and constantly changing environment in which they must actively engage in information management and problem solving while adapting to complex circumstances. In this challenging environment CIR consultants need to make critical decisions about what to advise clients that are impacted by a major cyber incident. Despite its relevance, CIR decision making is an understudied topic. The objective of this preliminary investigation is therefore to understand what decision-making strategies experienced CIR consultants use during challenging incidents and to offer suggestions for training and decision-aiding. A general understanding of operational decision making under pressure, uncertainty, and high stakes was established by reviewing the body of knowledge known as Naturalistic Decision Making (NDM). The general conclusion of NDM research is that experts usually make adequate decisions based on (fast) recognition of the situation and applying the most obvious (default) response pattern that has worked in similar situations in the past. In exceptional situations, however, this way of recognition-primed decision-making results in suboptimal decisions as experts are likely to miss conflicting cues once the situation is quickly recognized under pressure. Understanding the default response pattern and the rare occasions in which this response pattern could be ineffective is therefore key for improving and aiding cyber incident response decision making. Therefore, we interviewed six experienced CIR consultants and used the critical decision method (CDM) to learn how they made decisions under challenging conditions. The main conclusion is that the default response pattern for CIR consultants during cyber breaches is to reduce uncertainty as much as possible by gathering and investigating data and thus delay decision making about eradication until the investigation is completed. According to the respondents, this strategy usually works well and provides the most assurance that the threat actor can be completely removed from the network. However, the majority of respondents could recall at least one case in which this strategy (in hindsight) resulted in unnecessary theft of data or damage. Interestingly, this finding is strikingly different from other operational decision-making domains such as the military, police and fire service in which there is a general tendency to act rapidly instead of searching for more information. The main advice is that training and decision aiding of (novice) cyber incident responders should be aimed at the following: (a) make cyber incident responders aware of how recognition-primed decision making works; (b) discuss the default response strategy that typically works well in several scenarios; (c) explain the exception and how the exception can be recognized; (d) provide alternative response strategies that work better in exceptional situations.
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Indecent exposure is often regarded as a nuisance offense and detailed studies into this topic are relatively rare. However, there is consensus that relatively high recidivism rates and risk of escalation to more severe offenses can be of serious concern among these perpetrators. This cohort study aims to increase our general knowledge on the basic characteristics of these offenses and includes all registered police cases of indecent exposure in the Netherlands between 2012 and 2020, including 6741 incidents, involving 4663 suspects and 3808 registered victims. This first study of a large cohort over a long period of time describes the basic characteristics of these incidents, the perpetrators and their victims, and visualizes the results to explore trends over time. Results show that a modal indecent exposure incident is perpetrated by a 25-year-old male, on foot, on a public road, on a Wednesday afternoon in July, masturbating and directing his genitals intentionally toward a 13-year-old girl. The age distribution of victims shows remarkable similarity to victims of sexual assault. Compared to the first year of the period studied, the number of annually reported incidents gradually declined to half in the last year of the study. Findings are discussed in light of the most prominent theories on exhibitionism. Issues and suggestions relevant to apprehension and treatment of perpetrators are identified and discussed.
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Cybersecurity threat and incident managers in large organizations, especially in the financial sector, are confronted more and more with an increase in volume and complexity of threats and incidents. At the same time, these managers have to deal with many internal processes and criteria, in addition to requirements from external parties, such as regulators that pose an additional challenge to handling threats and incidents. Little research has been carried out to understand to what extent decision support can aid these professionals in managing threats and incidents. The purpose of this research was to develop decision support for cybersecurity threat and incident managers in the financial sector. To this end, we carried out a cognitive task analysis and the first two phases of a cognitive work analysis, based on two rounds of in-depth interviews with ten professionals from three financial institutions. Our results show that decision support should address the problem of balancing the bigger picture with details. That is, being able to simultaneously keep the broader operational context in mind as well as adequately investigating, containing and remediating a cyberattack. In close consultation with the three financial institutions involved, we developed a critical-thinking memory aid that follows typical incident response process steps, but adds big picture elements and critical thinking steps. This should make cybersecurity threat and incident managers more aware of the broader operational implications of threats and incidents while keeping a critical mindset. Although a summative evaluation was beyond the scope of the present research, we conducted iterative formative evaluations of the memory aid that show its potential.
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Critical incident response (CIR) has evolved to require a high level of cultural competence, customization, and adaptability to meet the needs of client organizations while incorporating clinical best practices and current research. The Critical Incident Outcome Measure (CIOM) is a timely and pioneering evidence-based evaluative tool developed by Morneau Shepell over the course of a four-year period. The CIOM tool, based on the Workplace Outcomes Suite (WOS) tool originally developed in 2010, was developed in 2016 [Herlihy et.al., 2018]; beta tests and modifications, along with the publication of a validation paper, were completed in 2017; further feedback was incorporated and an implementation plan developed in 2018; and full program implementation began in 2019.
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In January 2017, relations between Greece and Turkey were under severe strain when warships from both sides engaged in a brief standoff near a pair of uninhabited Greek ‘islets’ in the Aegean, whose sovereignty is disputed by Turkey. Theoretically informed by the literature of foreign policy analysis, we examine how the Greek diplomats, military officers and political analysts interpreted Turkey’s behaviour at that particular time. The article considers the following research question: which factors, from a Greek point of view, explain Turkey’s foreign policy in the Aegean in January 2017? Our theoretical expectation is that, in the aftermath of the coup attempt in Turkey, Greek diplomats, military officers and political analysts would ascribe domestic calculations into Turkey’s activities. We employed Q- methodology to uncover socially shared perspectives on this topic. Based on our findings, we uncovered two viewpoints: (1) Turkey’s diachronic strategy in the Aegean and (2) the strongman style. According to the former and most widely shared viewpoint, a consistent ‘rationalist’ strategy to change the status quo in the Aegean explains Turkey’s behaviour. According to the second one, the belief system of Turkey’s leadership legitimises the use of force in the conduct of foreign policy.
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Aggressive incidents occur frequently in health care facilities, such as psychiatric care and forensic psychiatric hospitals. Previous research suggests that civil psychiatric inpatients may display more aggression than forensic inpatients. However, there is a lack of research comparing these groups on the incident severity, even though both frequency and severity of aggression influence the impact on staff members. The purpose of this study is to compare the frequency and severity of inpatient aggression caused by forensic and civil psychiatric inpatients in the same Dutch forensic psychiatric hospital. Data on aggressive incidents occurring between January 1, 2014, and December 31, 2017, were gathered from hospital files and analyzed using the Modified Overt Aggression Scale, including sexual aggression (MOAS+). Multilevel random intercept models were used to analyze differences between forensic and civil psychiatric patients in severity of aggressive incidents. In all, 3,603 aggressive incidents were recorded, caused by 344 different patients. Civil psychiatric patients caused more aggressive incidents than forensic patients and female patients caused more inpatient aggression compared with male patients. Female forensic patients were found to cause the most severe incidents, followed by female civil psychiatric patients. Male forensic patients caused the least severe incidents. The findings have important clinical implications, such as corroborating the need for an intensive treatment program for aggressive and disruptive civil psychiatric patients, as well as emphasizing the importance of gender-responsive treatment
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The Short-Term Assessment of Risk and Treatability: Adolescent Version (START:AV) is a risk assessment instrument for adolescents that estimates the risk of multiple adverse outcomes. Prior research into its predictive validity is limited to a handful of studies conducted with the START:AV pilot version and often by the instrument’s developers. The present study examines the START:AV’s field validity in a secure youth care sample in the Netherlands. Using a prospective design, we investigated whether the total scores, lifetime history, and the final risk judgments of 106 START:AVs predicted inpatient incidents during a 4-month follow-up. Final risk judgments and lifetime history predicted multiple adverse outcomes, including physical aggression, institutional violations, substance use, self-injury, and victimization. The predictive validity of the total scores was significant only for physical aggression and institutional violations. Hence, the short-term predictive validity of the START:AV for inpatient incidents in a residential youth care setting was partially demonstrated and the START:AV final risk judgments can be used to guide treatment planning and decision-making regarding furlough or discharge in this setting.
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Aim. This study aims to identify ways that allow studying how intellectually impaired persons showing challenging behaviour interact with space, without impacting their daily lives. Back-ground. Research about space that better suits these persons’ needs is challenging to conduct, since they may have difficulties expressing themselves verbally and are extremely sensitive to-wards sensory stimuli. Therefore, researchers collecting data may be disturbing and intrusive, and requires great caution. Tapping into existing data may be a promising alternative. Residential care organisations routinely collect data about residents during their regular work processes, such as personal information and incident registration. Also useful may be routinely collected spatial data, such as drawings and repair reports. This study explores how routinely collected data (RCD) can provide insight into how residents interact with space, without impacting their daily lives. Methods. We reflect on the possibilities of using RCD (related to resident or space) based on explorations in the context of a case study at a Dutch very-intensive-care facility. The data were analysed to identify general patterns, such as locations with a high density of incidents/repairs and verified initial findings by member checking with staff. Results. The RCD analysed provide a basic and relevant insight into incidents and repairs connected to challenging behaviour. However, most data were neither complete or relevant for analysis. Therefore, we dis-cussed the RCD were with staff and only then it was possible to draw conclusions regarding relevance of RCD and the residents-space interactions. Conclusions. Only in conjunction with an ex-tended approach on member checking the use of RCD seems relevant. RCD have little meaning of their own. But the combination of RCD with member checking seems to provide insight into the interaction between residents and space, without interfering with the residents’ daily lives.
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In recent years, drones have increasingly supported First Responders (FRs) in monitoring incidents and providing additional information. However, analysing drone footage is time-intensive and cognitively demanding. In this research, we investigate the use of AI models for the detection of humans in drone footage to aid FRs in tasks such as locating victims. Detecting small-scale objects, particularly humans from high altitudes, poses a challenge for AI systems. We present first steps of introducing and evaluating a series of YOLOv8 Convolutional Neural Networks (CNNs) for human detection from drone images. The models are fine-tuned on a created drone image dataset of the Dutch Fire Services and were able to achieve a 53.1% F1-Score, identifying 439 out of 825 humans in the test dataset. These preliminary findings, validated by an incident commander, highlight the promising utility of these models. Ongoing efforts aim to further refine the models and explore additional technologies.
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