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Community-police relations have gained increasing public attention during the past decade. The purpose of the present study was to better understand the relationship between perceived community support and police officer burnout and engagement.

Data were gathered via online survey from 117 officers from a city police department in the Southeastern United States.

Community support was negatively correlated with burnout and positively correlated with engagement. Moreover, multiple regression analyses showed that community support explained significant incremental variance in most dimensions of burnout and engagement, above and beyond demographic factors and community stressors. Qualitative results showed that police officers had mixed perceptions of how they were viewed by the general public, with more negative than positive responses. However, officers felt more positively perceived in their own communities, but concerns were raised that national events affected the perceptions of officers even in positive relationships with their communities. Finally, officers felt that public perceptions impacted their job satisfaction, job performance and personal lives.

The results have practical implications for how to encourage positive interactions between officers and their community, with recommendations for both law enforcement leaders and civilians.

This study is one of the few that highlights the officers' perspective on how public perceptions affect their work. This is important in understanding how to maximize quality community interactions while minimizing conditions that would increase burnout.

This paper aims to explore the impact of interprofessional healthcare collaboration among nurses on patient healthcare services in the United Arab Emirates (UAE).

Data were gathered through a randomly distributed questionnaire (N = 248), constructed using established scales or the variables under study. The sample consisted of nurses and patients from various hospitals and clinics across the UAE. The collected data were analyzed using SPSS (Version 28) and Amos (Version 29) software, employing factor analysis, reliability testing and mediation analysis.

The study reveals a positive relationship between swift trust (ST) and its dimensions with both team interactive behavior (TIB) and nurse team creativity (TC). TIB was found to significantly mediate the effect of ST on TC. Additionally, based on closed-ended questions, a positive correlation was observed between team task conflict (TTC) and TC. However, no significant impact of TTC on nurse TC was identified through open-ended questions.

This research presents a unique analysis of the influence of interprofessional collaboration on patient healthcare services in the UAE, offering valuable insights for policy improvement by enhancing nursing conditions. Furthermore, the study contributes to the existing literature by examining the relationship between ST, TIB, TTC and TC.

Research and experience show that evidence-based practice (EBP), i.e. using the best available knowledge in daily professional work, is difficult to achieve in social services. The purpose of this study is to understand the development of organizational EBP learning processes in daily work through workplace education for staff and managers of supported homes for people with cognitive disabilities. The authors examine how the EBP model and new knowledge are understood and made actionable in the workplace, applying theories of organizational learning.

The authors used empirical material collected from an EBP workplace education pilot in Sweden, as well as documents on national EBP implementation in Swedish social services. Before the pilot, a focus group interview was conducted with regional senior managers. Participating managers and staff were individually interviewed two to three years after the pilot.

The study illustrates how knowledge-based action emerged from education where EBP was interpreted, understood, reflected on, and tested, supported by codified EBP tools in the work context. The participants, when supervised, and when observing and questioning their own behaviors in practice, contributed to double-loop learning (DLL) processes. Codification of EBP knowledge into useful tools and socialization processes during education and workplace meetings was crucial in developing individual and group DLL and knowledge-based actions.

The bottom-up approach to EBP development and the adaptive contextual learning at the workplace gave new insights into organizational learning in social service workplaces.

The time-varying equivalent linear temperature gradient (ELTG) significantly affects the development of faulting and must therefore be accounted for in pavement design. The same is true for faulting of bonded concrete overlays of asphalt (BCOA) with slabs larger than 3 x 3 m. However, the evaluation of ELTG in Mechanistic-Empirical (ME) BCOA design is highly time-consuming. The use of an effective ELTG (EELTG) is an efficient alternative to calculating ELTG. In this study, a model to quickly evaluate EELTG was developed for faulting in BCOA for panels 3 m or longer in size, whose faulting is sensitive to ELTG.

A database of EELTG responses was generated for 144 BCOAs at 169 locations throughout the continental United States, which was used to develop a series of prediction models. Three methods were evaluated: multiple linear regression (MLR), artificial neural networks (ANNs), and multi-gene genetic programming (MGGP). The performance of each method was compared, considering both accuracy and model complexity.

It was shown that ANNs display the highest accuracy, with an R² of 0.90 on the validation dataset. MLR and MGGP models achieved R² of 0.73 and 0.71, respectively. However, these models consisted of far fewer free parameters as compared to the ANNs. The model comparison performed in this study highlights the need for researchers to consider the complexity of models so that their direct implementation is feasible.

This research produced a rapid EELTG prediction model for BCOAs that can be incorporated into the existing faulting model framework.

This study aims to contribute novel insights into understanding and mitigating the harmful consequences of abusive supervision (AS) by examining the association between AS experiences, revenge, forgiveness, and the moderating role of emotional intelligence (EI). The key argument is that employees' EI can influence the AS experience through affective processes, countering supervisors' abusive behaviors.

A between-person scenario-based experiment was conducted with 366 participants divided into AS and control groups. The study explored the association between AS experience and revenge/forgiveness, mediated by core affect (valence and activation). EI abilities were measured as a moderator. Data analysis examined the relationships and interactions among AS, revenge/forgiveness, EI, and affective experiences.

The study reveals significant findings indicating that AS experiences were positively associated with revenge and negatively associated with forgiveness. The mediation analysis confirmed the role of core affect in these relationships. EI emerged as a moderator, shaping the association between AS experiences and revenge/forgiveness. Importantly, participants with higher EI exhibited lower revenge intentions, demonstrating the potential of EI to mitigate the adverse effects of AS. Unexpectedly, individuals with high EI also expressed fewer forgiveness intentions.

This study provides a comprehensive understanding of how employees can effectively counterbalance the impact of AS through higher levels of strategic EI. Examining core affect as a mediator offers novel insights into coping mechanisms in response to AS experiences and their consequences.

The study acknowledges several limitations, as the scenarios may only partially capture the complexities of real-life AS situations. The focus on a specific context and the sample characteristics limit the generalizability of the findings. Future research should explore diverse organizational contexts and employ longitudinal designs.

The findings have practical implications for organizations as enhancing employees' EI skills through training programs interventions and integrating EI into organizational culture and leadership conduct.

This chapter aims to consider workplace spirituality from a cross-cultural perspective. The terms workplace spirituality and culture are so intangible, and both start with the attitude and behavior of humans but are meaningful in a community. Because of the constraint of understanding these abstract settings, it is necessary to define and specify the dimensions of both concepts to achieve the aim of this chapter. In this regard, the section starts with the conceptuality of workplace spirituality and the dimensions of the term, which cumulate at individual, group, and organizational levels. It goes with the part of cultural dimensions in the light of Hofstede's (2001) direction, Chhokar, Brodbeck, and House's (2007) extension, and Sharma's (2010) derivation of cultural dimensions for national, organizational, and individual levels, respectively. After joining the dots, the chapter focuses on one of the most sacred research areas for academic literature, cross-cultural differences, and workplace spirituality. The last part of the chapter is the conclusion to point to final notes about the concepts and help guide future studies.

The use of generative artificial intelligence (genAi) language models such as ChatGPT to write assignment text is well established. This paper aims to assess to what extent genAi can be used to obtain guidance on how to avoid detection when commissioning and submitting contract-written assignments and how workable the offered solutions are.

Although ChatGPT is programmed not to provide answers that are unethical or that may cause harm to people, ChatGPT’s can be prompted to answer with inverted moral valence, thereby supplying unethical answers. The authors tasked ChatGPT to generate 30 essays that discussed the benefits of submitting contract-written undergraduate assignments and outline the best ways of avoiding detection. The authors scored the likelihood that ChatGPT’s suggestions would be successful in avoiding detection by markers when submitting contract-written work.

While the majority of suggested strategies had a low chance of escaping detection, recommendations related to obscuring plagiarism and content blending as well as techniques related to distraction have a higher probability of remaining undetected. The authors conclude that ChatGPT can be used with success as a brainstorming tool to provide cheating advice, but that its success depends on the vigilance of the assignment markers and the cheating student’s ability to distinguish between genuinely viable options and those that appear to be workable but are not.

This paper is a novel application of making ChatGPT answer with inverted moral valence, simulating queries by students who may be intent on escaping detection when committing academic misconduct.