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Clinical staff working in mental health services experience high levels of work-related stress, burnout and poor well-being. Increased levels of stress, burnout, depression and anxiety and poorer mental well-being among health-care workers are associated with more sick days, absenteeism, lower work satisfaction, increased staff turnover and reduced quality of patient care. Virtual reality (VR) relaxation is a technique whereby experiences of pleasant and calming environments are accessed through a head-mounted display to promote relaxation. The purpose of this paper is to describe the design of a study that assesses the feasibility and acceptability of implementing a multi-session VR relaxation intervention amongst mental health professionals, to improve their relaxation levels and mental well-being.

The study follows a pre–post-test design. Mental health staff will be recruited for five weeks of VR relaxation. The authors will measure the feasibility and acceptability of the VR relaxation intervention as primary outcomes, alongside secondary outcomes evaluating the benefits of VR relaxation for mental well-being.

The study aims to recruit 20–25 health-care professionals working in both inpatient and specialist community mental health settings.

Research indicates the potential of VR relaxation as a low-intensity intervention to promote relaxation and reduce stress in the workplace. If VR relaxation is shown to be feasible and acceptable, when delivered across multiple sessions, there would be scope for large-scale work to investigate its effectiveness as an approach to enable health-care professionals to de-stress, relax and optimise their mental well-being. In turn, this may consequently reduce turnover and improve stress-related sick leave across health-care services.

This study aims to view police mental and physical health and overall well-being through a victimological lens so as to attempt to prevent problems from starting or protecting them by informing them of what may occur within their career.

Knowledge production within the field of police health and career implications is exponentially increasing as officers all over the world try and sometimes fail to navigate the difficulties of their complex career choice. Many of the disciplines that deal with this research are acting as silos, so there is not a lot of crossover in Australian literature. This study creates a contemporary collective of literary evidence in relation to police well-being as well as the impact of COVID on them. Creating this collective is why the literature review as a research method is critical. Traditional literature reviews can lack clear process. By using a literature review as a specific methodology, the outcome is a meticulous record of all relevant materials.

The results of this literature review identified, without bias or interpretation, many officers became disillusioned, mentally unwell and took time away from work for two main reasons: (1) for many police officers, the substantial distress from cumulative exposure to bureaucratic administration and management styles, erratic work hours and long hours of repetitive work and (2) the dangers of day-to-day policing with the presence at fatal accidents, suicides, receiving threats to life, being assaulted and gaining poor eating and drinking habits creating issues for sleep and physical health.

For the purposes of creating a contemporary paper, the authors restricted the sample of literature to 22 years (accessing from 2,000 onward). By only selecting journals from Google Scholar, relating to specific years and drawing on search terms to limit our search, it may be perceived to have skewed the sample and the outcomes. Further work will be completed in the future to correct this.

Police organisations may consider altering their bureaucratic procedures and make an effort to allow officers to better self-manage minor issues. From a victimological perspective, given that police officers are more than likely to be affected by cumulative experience of traumatic events over their career, they should be taught how to lower their individual levels of stress, to practice self-care and to be able to trust that the care they seek will be readily available without judgement.

Knowing the triggers related to police breakdown, both physically and mentally, may help intervene in the early years to prevent The extremes of policing range from being faced with overwhelming paperwork and administration to acute trauma events and can leave the officer dealing with cumulative stress in all its guises. Allowing a judgment free public debate into this issue will assist police (and other emergency service works) in the future.

Viewing police officers as victims of their career choice is not common and reviewing the factors that impact them on a daily basis and throughout their career is critical for both prevention and understanding. This paper has value to numerous disciplines.

Purchase conversion prediction aims to improve user experience and convert visitors into real buyers to drive sales of firms; however, the total conversion rate is low, especially for e-retailers. To date, little is known about how e-retailers can scientifically detect users' intents within a purchase conversion funnel during their ongoing sessions and strategically optimize real-time marketing tactics corresponding to dynamic intent states. This study mainly aims to detect a real-time state of the conversion funnel based on graph theory, which refers to a five-class classification problem in the overt real-time choice decisions (RTCDs)—click, tag-to-wishlist, add-to-cart, remove-from-cart and purchase—during an ongoing session.

The authors propose a novel graph-theoretic framework to detect different states of the conversion funnel by identifying a user's unobserved mindset revealed from their navigation process graph, namely clickstream graph. First, the raw clickstream data are identified into individual sessions based on a 30-min time-out heuristic approach. Then, the authors convert each session into a sequence of temporal item-level clickstream graphs and conduct a temporal graph feature engineering according to the basic, single-, dyadic- and triadic-node and global characteristics. Furthermore, the synthetic minority oversampling technique is adopted to address with the problem of classifying imbalanced data. Finally, the authors train and test the proposed approach with several popular artificial intelligence algorithms.

The graph-theoretic approach validates that users' latent intent states within the conversion funnel can be interpreted as time-varying natures of their online graph footprints. In particular, the experimental results indicate that the graph-theoretic feature-oriented models achieve a substantial improvement of over 27% in line with the macro-average and micro-average area under the precision-recall curve, as compared to the conventional ones. In addition, the top five informative graph features for RTCDs are found to be Transitivity, Edge, Node, Degree and Reciprocity. In view of interpretability, the basic, single-, dyadic- and triadic-node and global characteristics of clickstream graphs have their specific advantages.

The findings suggest that the temporal graph-theoretic approach can form an efficient and powerful AI-based real-time intent detecting decision-support system. Different levels of graph features have their specific interpretability on RTCDs from the perspectives of consumer behavior and psychology, which provides a theoretical basis for the design of computer information systems and the optimization of the ongoing session intervention or recommendation in e-commerce.

To the best of the authors' knowledge, this is the first study to apply clickstream graphs and real-time decision choices in conversion prediction and detection. Most studies have only meditated on a binary classification problem, while this study applies a graph-theoretic approach in a five-class classification problem. In addition, this study constructs temporal item-level graphs to represent the original structure of clickstream session data based on graph theory. The time-varying characteristics of the proposed approach enhance the performance of purchase conversion detection during an ongoing session.

Session-based recommendation aims to predict the user's next preference based on the user's recent activities. Although most existing studies consider the global characteristics of items, they only learn the global characteristics of items based on a single connection relationship, which cannot fully capture the complex transformation relationship between items. We believe that multiple relationships between items in learning sessions can improve the performance of session recommendation tasks and the scalability of recommendation models. At the same time, high-quality global features of the item help to explore the potential common preferences of users.

This work proposes a session-based recommendation method with a multi-relation global context–enhanced network to capture this global transition relationship. Specifically, we construct a multi-relation global item graph based on a group of sessions, use a graded attention mechanism to learn different types of connection relations independently and obtain the global feature of the item according to the multi-relation weight.

We did related experiments on three benchmark datasets. The experimental results show that our proposed model is superior to the existing state-of-the-art methods, which verifies the effectiveness of our model.

First, we construct a multi-relation global item graph to learn the complex transition relations of the global context of the item and effectively mine the potential association of items between different sessions. Second, our model effectively improves the scalability of the model by obtaining high-quality item global features and enables some previously unconsidered items to make it onto the candidate list.

This study explores one-to-one LEGO® Serious Play® in positive psychology coaching (1-1 LSP in PPC) as an intervention to help emerging adults (EAs) in higher education develop a growth mindset.

This is a qualitative single-participant case study of an EA undergraduate student's experience with 1-1 LSP in PPC to help him navigate uncertainty about making a decision that he felt would influence his future career.

1-1 LSP in PPC enabled the participant to create a metaphoric representation of how a growth mindset operated for him, promoting self-awareness and reflectivity. The LEGO® model that the participant built during his final session acted as a reminder of the resources and processes he developed during coaching, which helped him navigate future challenges.

This study contributes to the emerging literature on the impact of using LSP as a tool in one-to-one coaching in higher education. The participant's experience demonstrates that 1-1 LSP in PPC may be an effective way to support positive EA development. More research is needed to explore its potential.

This study provides a possible roadmap to incorporate 1-1 LSP in PPC into coaching in higher education as a reflective tool to build a growth mindset in EA students.

Because most undergraduates are EAs navigating the transition from adolescence into adulthood, universities would benefit from adopting developmentally informed coaching practices. 1-1 LSP in PPC may be an effective intervention that provides the structured and psychologically safe environment EAs need to develop lasting personal resources.

This study focused on unpacking the instructional coaching process, addressing key questions about what happens during a coaching session and what coaching elements predict teacher outcomes.

Using coaching observational data, the research examined critical coaching processes described in the literature: coaching practices (observation, feedback, reflective discussion and planning), the coach–teacher relationship, coaching strategies and coaching duration. The study also developed a path model documenting how coaching behaviors predicted teacher instruction.

Results showed that the coach talked more than the teacher and that most coaching time was spent in reflective discussion. The coach–teacher relationship was promoted by building rapport and reciprocal trust, with use of “we” language demonstrating that coach and teacher were working as a partnership. Most common coaching strategies were clarifying and the coach prompting the teacher to attend to teacher or student behaviors. Path model analysis showed that (a) the coach–teacher relationship quality predicted the level of teacher engagement in coaching and their instructional reflection and (b) the quality of coaching strategies predicted the overall quality of the classroom instruction.

The study provides empirical evidence about the active ingredients of coaching – those underlying processes that impact and improve teacher practice.

Research into responsible management education has largely focused on the merits, attributes, and transformation opportunities to enhance responsible business school education aims. As such, a prominent part of the literature has occupied itself with examining if responsible management modules are inherently considered a non-crucial element of the curriculum and determining the extent to which business schools have introduced such learning content into their curriculum. However, there has been scant research into how to apply novel teaching approaches to engage students and promote responsible management education endeavours. As such, this paper seeks to address this gap through the development of a teaching framework to support educators in designing effective learning environments focused on responsible management education. We draw on constructivist learning theories and Lego Serious Play (LSP) as a learning enhancement approach to develop a pedagogical framework titled The Educator's LSP Journey. LSP is chosen due to its increasing application in learning environments to help promote critical discourse, and engage with highly complex problems, whether these are social, economic, environmental, or organisational. Therefore, this paper contributes to the responsible management education discourse by providing educators with a practical methodology to support student engagement and co-creation of knowledge by fostering exploratory learning environments and enriching the practices of active learning communities.

The author presents new estimates of the probability weighting functions found in rank-dependent theories of choice under risk. These estimates are unusual in two senses. First, they are free of functional form assumptions about both utility and weighting functions, and they are entirely based on binary discrete choices and not on matching or valuation tasks, though they depend on assumptions concerning the nature of probabilistic choice under risk. Second, estimated weighting functions contradict widely held priors of an inverse-s shape with fixed point well in the interior of the (0,1) interval: Instead the author usually finds populations dominated by “optimists” who uniformly overweight best outcomes in risky options. The choice pairs used here mostly do not provoke similarity-based simplifications. In a third experiment, the author shows that the presence of choice pairs that provoke similarity-based computational shortcuts does indeed flatten estimated probability weighting functions.

The purpose of this paper is to report on the implementation of the World Health Organization (WHO) trauma care checklist (TCC) (WHO, 2016) in an emergency department in a tertiary hospital in Nepal. This research was undertaken as part of a Hybrid International Emergency Medicine Fellowship programme (Subedi et al., 2020) across UK and Nepal, incorporating a two-year rotation through the UK National Health Service, via the Medical Training Initiative (MTI) (AoMRC, 2017). The WHO TCC can improve outcomes for trauma patients (Lashoher et al., 2016); however, significant barriers affect its implementation worldwide (Nolan et al., 2014; Wild et al., 2020). This article reports on the implementation, barriers and recommendations of WHO TCC implementation in the context of Nepal and argues for Transformational Leadership (TL) to support its implementation.

Explanatory mixed methods research (Creswell, 2014), comprising quasi-experimental research and a qualitative online survey, were selected methods for this research. A training module was designed and implemented for 10 doctors and 15 nurses from a total of 76 (33%) of clinicians to aid in the introduction of the WHO TCC in an emergency department in a hospital in Nepal. The quasi-experimental research involved a pre- and post-training survey aimed to assess participant’s knowledge of the WHO TCC before and after training and before the implementation of the WHO TCC in the emergency department. Post-training, 219 patients were reviewed after four weeks to identify if process measures had improved the quality of care to trauma patients. Subsequently six months later, a qualitative online survey was sent to all clinical staff in the department to identify barriers to implementation, with a response rate of 26 (n = 26) (34%) (20 doctors and 6 nurses). Descriptive statistics were used to evaluate quantitative data and the qualitative data were analysed using the five stepped approach of thematic analysis (Braun and Clarke, 2006).

The evaluation of the implementation of the WHO TCC showed an improvement in care for trauma patients in an emergency setting in a tertiary hospital in Nepal. There were improvements in the documentation in trauma management, showing the training had a direct impact on the quality of care of trauma patients. Notably, there was an improvement in cervical spine examination from 56.1% before training to 78.1%; chest examination 125 (57.07%) before training and 170 (77.62%) post-training; abdominal examination 121 (55.25%) before training and 169 (77.16%) post-training; gross motor examination 13 (5.93%) before training and 131 (59.82%) post-training; sensory examination 4 (1.82%) before training and 115 (52.51%) post-training; distal pulse examination 6 (2.73%) before training and 122 (55.7%) post-training. However, while the quality of documentation for trauma patients improved from the baseline of 56%, it only reached 78% when the percentage improvement target agreed for this research project was 90%. The 10 (n = 10) doctors and 15 (n = 15) nurses in the Emergency Department (ED) all improved their baseline knowledge from 72.2% to 87% (p = 0.00006), by 14.8% and 67% to 85%) (p = 0.006), respectively. Nurses started with lower scores (mean 67) in the baseline when compared to doctors, but they made significant gains in their learning post-training. The qualitative data reported barriers, such as the busyness of the department, with residents and medical officers, suggesting a shortened version of the checklist to support greater protocol compliance. Embedding this research within TL provided a steer for successful innovation and change, identifying action for sustaining change over time.

The study is a single-centre study that involved trauma patients in an emergency department in one hospital in Nepal. There is a lack of internationally recognised trauma training in Nepal and very few specialist trauma centres; hence, it was challenging to teach trauma to clinicians in a single 1-h session. High levels of transformation of health services are required in Nepal, but the sample for this research was small to test out and pilot the protocol to gain wider stakeholder buy in. The rapid turnover of doctors and nurses in the emergency department, creates an additional challenge but encouraging a multi-disciplinary approach through TL creates a greater chance of sustainability of the WHO TCC.

International protocols are required in Nepal to support the transformation of health care. This explanatory mixed methods research, which is part of an International Fellowship programme, provides evidence of direct improvements in the quality of patient care and demonstrates how TL can drive improvement in a low- to medium-income country.

The Nepal/UK Hybrid International Emergency Medicine Fellowships have an opportunity to implement changes to the health system in Nepal through research, by bringing international level standards and protocols to the hospital to improve the quality of care provided to patients.

To the best of the authors’ knowledge, this research paper is one of the first studies of its kind to demonstrate direct patient level improvements as an outcome of the two-year MTI scheme.

User feedback inferred from the user's search-time behavior could improve the learning to rank (L2R) algorithms. Click models (CMs) present probabilistic frameworks for describing and predicting the user's clicks during search sessions. Most of these CMs are based on common assumptions such as Attractiveness, Examination and User Satisfaction. CMs usually consider the Attractiveness and Examination as pre- and post-estimators of the actual relevance. They also assume that User Satisfaction is a function of the actual relevance. This paper extends the authors' previous work by building a reinforcement learning (RL) model to predict the relevance. The Attractiveness, Examination and User Satisfaction are estimated using a limited number of the features of the utilized benchmark data set and then they are incorporated in the construction of an RL agent. The proposed RL model learns to predict the relevance label of documents with respect to a given query more effectively than the baseline RL models for those data sets.

In this paper, User Satisfaction is used as an indication of the relevance level of a query to a document. User Satisfaction itself is estimated through Attractiveness and Examination, and in turn, Attractiveness and Examination are calculated by the random forest algorithm. In this process, only a small subset of top information retrieval (IR) features are used, which are selected based on their mean average precision and normalized discounted cumulative gain values. Based on the authors' observations, the multiplication of the Attractiveness and Examination values of a given query–document pair closely approximates the User Satisfaction and hence the relevance level. Besides, an RL model is designed in such a way that the current state of the RL agent is determined by discretization of the estimated Attractiveness and Examination values. In this way, each query–document pair would be mapped into a specific state based on its Attractiveness and Examination values. Then, based on the reward function, the RL agent would try to choose an action (relevance label) which maximizes the received reward in its current state. Using temporal difference (TD) learning algorithms, such as Q-learning and SARSA, the learning agent gradually learns to identify an appropriate relevance label in each state. The reward that is used in the RL agent is proportional to the difference between the User Satisfaction and the selected action.

Experimental results on MSLR-WEB10K and WCL2R benchmark data sets demonstrate that the proposed algorithm, named as SeaRank, outperforms baseline algorithms. Improvement is more noticeable in top-ranked results, which usually receive more attention from users.

This research provides a mapping from IR features to the CM features and thereafter utilizes these newly generated features to build an RL model. This RL model is proposed with the definition of the states, actions and reward function. By applying TD learning algorithms, such as the Q-learning and SARSA, within several learning episodes, the RL agent would be able to learn how to choose the most appropriate relevance label for a given pair of query–document.