RESUMEN

The global adoption of e-scooters as a convenient mode of micro-mobility transportation is on the rise, offering a flexible solution for covering first- and last-mile journeys. However, this surge in usage brings challenges, particularly concerning road safety, as e-scooter riders often share road space with other vehicles, heightening the risk of serious accidents. While numerous studies have explored safe overtaking behaviors and safety perceptions from drivers' viewpoints, limited attention has been given to understanding the varying safety perceptions of both drivers and e-scooter riders, particularly after riding an e-scooter and being overtaken by their own vehicles. This research aims to bridge this gap by examining variations in safety perceptions and assessing behavioral changes before and after experiencing overtaking scenarios. Specifically, the study focuses on scenarios where an e-scooter rider experiences being overtaken by a vehicle they had previously driven. A Unity-based sequential simulation process is employed to replay scenarios obtained from a vehicle simulator during an e-scooter experiment involving the same participant without their awareness. This innovative approach allows e-scooter rider participants to relive their own prior vehicle overtaking maneuvers while riding an e-scooter. The findings reveal that most participants (64%) felt less safe as e-scooter riders, influenced by factors like relative speed and acceleration of overtaking vehicles. After experiencing being overtaken by their own pre-driven vehicles, a noteworthy positive correlation emerged between safety perception and lateral distance, indicating that greater distance is derived from a better understanding of e-scooter safety. The study demonstrates the effectiveness of the sequential simulation strategy in fostering safe driving behavior and raising road safety awareness. Experiencing overtaking behaviors firsthand as an e-scooter rider, previously behind the wheel of the overtaking vehicle, encourages a heightened awareness of road safety. These findings have significant implications for road safety authorities, suggesting the potential application of this approach in driver education programs. By incorporating such interventions tailored to improve the safety of vulnerable road users, authorities can take proactive steps towards mitigating risks associated with micro-mobility transportation.

Asunto(s)

RESUMEN

A new non-stationary, high-order sequential simulation method is presented herein, aiming to accommodate complex curvilinear patterns when modelling non-Gaussian, spatially distributed and variant attributes of natural phenomena. The proposed approach employs spatial templates, training images and a set of sample data. At each step of a multi-grid approach, a template consisting of several data points and a simulation node located in the center of the grid is selected. To account for the non-stationarity exhibited in the samples, the data events decided by the conditioning data are utilized to calibrate the importance of the related replicates. Sliding the template over the training image generates a set of training patterns, and for each pattern a weight is calculated. The weight value of each training pattern is determined by a similarity measure defined herein, which is calculated between the data event of the training pattern and that of the simulation pattern. This results in a non-stationary spatial distribution of the weight values for the training patterns. The proposed new similarity measure is constructed from the high-order statistics of data events from the available data set, when compared to their corresponding training patterns. In addition, this new high-order statistics measure allows for the effective detection of similar patterns in different orientations, as these high-order statistics conform to the commutativity property. The proposed method is robust against the addition of more training images due to its non-stationary aspect; it only uses replicates from the pattern database with the most similar local high-order statistics to simulate each node. Examples demonstrate the key aspects of the method.

RESUMEN

A training image free, high-order sequential simulation method is proposed herein, which is based on the efficient inference of high-order spatial statistics from the available sample data. A statistical learning framework in kernel space is adopted to develop the proposed simulation method. Specifically, a new concept of aggregated kernel statistics is proposed to enable sparse data learning. The conditioning data in the proposed high-order sequential simulation method appear as data events corresponding to the attribute values associated with the so-called spatial templates of various geometric configurations. The replicates of the data events act as the training data in the learning framework for inference of the conditional probability distribution and generation of simulated values. These replicates are mapped into spatial Legendre moment kernel spaces, and the kernel statistics are computed thereafter, encapsulating the high-order spatial statistics from the available data. To utilize the incomplete information from the replicates, which partially match the spatial template of a given data event, the aggregated kernel statistics combine the ensemble of the elements in different kernel subspaces for statistical inference, embedding the high-order spatial statistics of the replicates associated with various spatial templates into the same kernel subspace. The aggregated kernel statistics are incorporated into a learning algorithm to obtain the target probability distribution in the underlying random field, while preserving in the simulations the high-order spatial statistics from the available data. The proposed method is tested using a synthetic dataset, showing the reproduction of the high-order spatial statistics of the sample data. The comparison with the corresponding high-order simulation method using TIs emphasizes the generalization capacity of the proposed method for sparse data learning.

RESUMEN

Background: Asthma is the most common chronic disease of childhood and an important preventable cause of mortality in children and young people (CYP). Few studies have brought together CYP and health professionals to understand the patient perspective of routine asthma care. We sought to explore how young people engage with routine asthma care in North West London through sequential simulation. Method: We designed a sequential simulation focusing on routine asthma management in young people aged 12-18. A 20 min simulation was developed with four young people to depict typical interactions with school nurses and primary care services. This was performed to a mixed audience of young people, general practitioners (GPs), paediatricians, school nurses and commissioners. Young people were invited to attend by their GPs and through social media channels. Attendees participated in audio-recorded, facilitated discussions exploring the themes arising from the simulation. Recordings were transcribed and subjected to thematic analysis. Results: 37 people attended the sequential simulation. Themes arising from postsimulation discussions included recognition of chaotic family lifestyles as a key barrier to accessing care; the importance of strong communication between multidisciplinary team professionals and recognition of the role school nurses can play in delivering routine asthma care. Conclusion: Sequential simulation allows healthcare providers to understand routine asthma care for CYP from the patient perspective. We propose improved integration of school nurses into routine asthma care and regular multidisciplinary team meetings to reduce fragmentation, promote interprofessional education and address the widespread professional complacency towards this lethal condition.

RESUMEN

The rapid spread of the SARS-CoV-2 epidemic has simultaneous time and space dynamics. This behaviour results from a complex combination of factors, including social ones, which lead to significant differences in the evolution of the spatiotemporal pattern between and within countries. Usually, spatial smoothing techniques are used to map health outcomes, and rarely uncertainty of the spatial predictions are assessed. As an alternative, we propose to apply direct block sequential simulation to model the spatial distribution of the COVID-19 infection risk in mainland Portugal. Given the daily number of infection data provided by the Portuguese Directorate-General for Health, the daily updates of infection rates are calculated by municipality and used as experimental data in the geostatistical simulation. The model considers the uncertainty/error associated with the size of each municipality's population. The calculation of daily updates of the infection risk maps results from the median model of one ensemble of 100 geostatistical realizations of daily updates of the infection risk. The ensemble of geostatistical realizations is also used to calculate the associated spatial uncertainty of the spatial prediction using the interquartile distance. The risk maps are updated daily and show the regions with greater risks of infection and the critical dynamics related to its development over time.

Asunto(s)

RESUMEN

BACKGROUND: Knife-related behaviour among young people is an increasing social concern with a total of 35 teenagers killed by knife attacks in England in 2017. Distributed simulation has been shown to be a valid method of portable simulation for medical professionals; however, its role in delivering a socially educational message to members of the public has not been previously studied. This paper explores how the novel use of simulation could be used to address a serious social issue amongst young people at risk of criminal knife behaviour. METHODS: A qualitative approach was used to study a two-part workshop attended by two groups of young people vulnerable to knife crime. Based on the concepts of sequential simulation and distributed simulation previously developed at the Imperial College Centre for Engagement and Simulation Science, the first part of the workshop showed the patient journey of a young man stabbed in the abdomen, attended by policemen and paramedics, followed by the participants witnessing a simulated emergency abdominal operation on a silicone model and concluded with a dialogue between the surgeon, the victim (who required an intestinal stoma as a result of the knife injury) and his mother. The second part of the workshop involved further discussion with the participants regarding the role of knives from the personal and community perspective. Visual data was recorded during the workshops and qualitative data obtained from group and individual interviews were thematically analysed. RESULTS: A total of sixty teenagers aged 13-19 took part in the two workshops. The participant feedback suggested that the workshops provided a safe environment where young people could learn about and explore the consequences surrounding knife crime. Furthermore, participant recollection of key points was assessed between 4 and 6 weeks after the second workshop and the data suggested that the workshop could promote learning and a change in the participants' knife-related behaviour in the future. CONCLUSIONS: The findings support further exploration of simulation as a modality for engaging young people about the issues surrounding criminal knife behaviour in a safe and cooperative environment. Moreover, the findings suggest that the workshop could be used as an educational tool that may facilitate behavioural change.

RESUMEN

BACKGROUND: A need for improved education and training for hospital staff caring for patients in the last year of life was identified at an urban UK hospital. Sequential Simulation (SqS Simulation™) is a type of simulation that recreates a patient's journey, considering the longitudinal element of care and how this might impact on the patient's experiences, wishes and needs. OBJECTIVES: The aim of this study was to investigate a new end of life care training intervention for multi-professional hospital staff, and its effect on their confidence in managing patients at the end of their life. SETTING/PARTICIPANTS: Based on the results of a formal Training Needs Analysis, four SqS Simulation™ specialty-based courses were designed for general medical and surgical multidisciplinary teams in an acute UK hospital. METHODS: Over three months, seven SqS Simulation™ sessions were attended by fifty-seven multidisciplinary healthcare professionals. A quasi-experimental mixed-methods study was conducted using open and closed-ended questionnaires, pre and post-intervention. Changes in course attendees' confidence levels were analysed and qualitative data from free-text answers informed potential reasons for any differences identified. RESULTS: Confidence improved for all professional cohorts (p < 0.001). The differences were found to be highly significant for 'doctors' (p < 0.001), significant for 'therapists' (p = 0.02) and not significant for the 'nurses' cohort (p = 0.238). This was explored further using a qualitative explanatory framework. Categories included: Communicating with Families; Teamwork; Goal Planning; Do Not Attempt Cardiopulmonary Resuscitation; Course Usefulness; Prior Training; and Clinical Experience. CONCLUSION: This study has shown an overall improvement in confidence across disciplines after attending a SqS Simulation™ course. The differences in quantitative results between disciplines were explored through the qualitative data and revealed a difference in what the professionals gained from it. Further studies are required to assess its effectiveness in maintaining confidence of end of life care in practice, as well as its benefit to patient outcomes.

Asunto(s)

RESUMEN

Background: A new challenge for healthcare managers is to improve the patient experience. Simulation is often used for clinical assessment and rarely for those operating outside of direct clinical care. Sequential simulation (SqS) is a form of simulation that re-creates care pathways, widening its potential use. Local problem: Numbers, outcome measures and system profiling are used to inform healthcare decisions. However, none of these captures the personal subtleties of a patient's experience. Intervention: 56 students attended a teaching module using SqS and facilitated workshops as part of their induction week on an MSc International Health Management course. The workshop was voluntary and was offered as an opportunity for the students to gain an insight into the UK health system through the medium of simulation. Methods: An evaluation survey incorporating quantitative and qualitative student feedback was conducted. Descriptive statistics were generated from the quantitative data, and thematic analysis was undertaken for the qualitative data. Results: There was strong agreement for the acceptability of the workshop approach in relation to the aims and objectives. Likert scale (1--5) mean total=4.49. Participants responded enthusiastically (revealed through the qualitative data) with ideas related to perspectives sharing, understanding healthcare management and processes and the consideration of feasibility and practicalities. They also suggested other applications that SqS could be used for. Conclusion: The SqS approach has demonstrated that simulation has a wider potential than for clinical assessment alone. Further studies are required to determine its potential uses and affordances beyond its current format.

RESUMEN

Background: The Department of health funded an initiative to pioneer new approaches that would create a more integrated form of care. Local problem: In order to receive funding, local Clinical Commissioning Groups were required to engage a range of stakeholders in a practical approach that generated the development of an integrated model of care. Intervention: Two sequential simulation (SqS) workshops comprising 65 and 93 participants, respectively, were designed using real patient scenarios from the locality, covering areas of general practice, community health and adult social care. Workshops were attended by a diverse group of stakeholders. The first workshop addressed current care pathways and the second modelled ideal care pathways generated from the data obtained at the first workshop. Methods: Discussions were captured through video recording, field-notes and pre and post questionnaires. Data was collated, transcribed and analysed through a combination of descriptive statistics and thematic analysis. Results: The questionnaires revealed that attendees strongly agreed that they had had an opportunity to contribute to all discussions and raise questions, concerns and ideas (100%). Pre and post knowledge of current and new models of care was vastly improved. The opportunity to share information and to network was valued, with the SqS approach seen as breaking professional barriers (100%). Conclusions: Simulation can be used as a tool to engage stakeholders in designing integrated models of care. The systematic data collection from the diverse ideas generated also allows for a much-needed 'ear' to those providing the solutions, as well as a legitimate and balanced perspective.

RESUMEN

This paper proposes simulation-based enactment of care as an innovative and fruitful means of engaging patients and clinicians to create collaborative solutions to healthcare issues. This use of simulation is a radical departure from traditional transmission models of education and training. Instead, we frame simulation as co-development, through which professionals, patients and publics share their equally (though differently) expert perspectives. The paper argues that a process of participatory design can bring about new insights and that simulation offers understandings that cannot easily be expressed in words. Drawing on more than a decade of our group's research on simulation and engagement, the paper summarises findings from studies relating to clinician-patient collaboration and proposes a novel approach to address the current need. The paper outlines a mechanism whereby pathways of care are jointly created, shaped, tested and refined by professionals, patients, carers and others who are affected and concerned by clinical care.

RESUMEN

BACKGROUND: Simulation is firmly established as a mainstay of clinical education, and extensive research has demonstrated its value. Current practice uses inanimate simulators (with a range of complexity, sophistication and cost) to address the patient 'as body' and trained actors or lay people (Simulated Patients) to address the patient 'as person'. These approaches are often separate.Healthcare simulation to date has been largely for the training and assessment of clinical 'insiders', simulating current practices. A close coupling with the clinical world restricts access to the facilities and practices of simulation, often excluding patients, families and publics. Yet such perspectives are an essential component of clinical practice. MAIN BODY: This paper argues that simulation offers opportunities to move outside a clinical 'insider' frame and create connections with other individuals and groups. Simulation becomes a bridge between experts whose worlds do not usually intersect, inviting an exchange of insights around embodied practices-the 'doing' of medicine-without jeopardising the safety of actual patients.Healthcare practice and education take place within a clinical frame that often conceals parallels with other domains of expert practice. Valuable insights emerge by viewing clinical practice not only as the application of medical science but also as performance and craftsmanship.Such connections require a redefinition of simulation. Its essence is not expensive elaborate facilities. Developments such as hybrid, distributed and sequential simulation offer examples of how simulation can combine 'patient as body' with 'patient as person' at relatively low cost, democratising simulation and exerting traction beyond the clinical sphere.The essence of simulation is a purposeful design, based on an active process of selection from an originary world, abstraction of what is criterial and re-presentation in another setting for a particular purpose or audience. This may be done within traditional simulation centres, or outside in local communities, public spaces or arts and performance venues. CONCLUSIONS: Simulation has established a central role in clinical education but usually focuses on learning to do things as they are already done. Imaginatively designed, simulation offers untapped potential for deep engagement with patients, publics and experts outside medicine.

RESUMEN

Coal seam degasification improves coal mine safety by reducing the gas content of coal seams and also by generating added value as an energy source. Coal seam reservoir simulation is one of the most effective ways to help with these two main objectives. As in all modeling and simulation studies, how the reservoir is defined and whether observed productions can be predicted are important considerations. Using geostatistical realizations as spatial maps of different coal reservoir properties is a more realistic approach than assuming uniform properties across the field. In fact, this approach can help with simultaneous history matching of multiple wellbores to enhance the confidence in spatial models of different coal properties that are pertinent to degasification. The problem that still remains is the uncertainty in geostatistical simulations originating from the partial sampling of the seam that does not properly reflect the stochastic nature of coal property realizations. Stochastic simulations and using individual realizations, rather than E-type, make evaluation of uncertainty possible. This work is an advancement over Karacan et al. (2014) in the sense of assessing uncertainty that stems from geostatistical maps. In this work, we batched 100 individual realizations of 10 coal properties that were randomly generated to create 100 bundles and used them in 100 separate coal seam reservoir simulations for simultaneous history matching. We then evaluated the history matching errors for each bundle and defined the single set of realizations that would minimize the error for all wells. We further compared the errors with those of E-type and the average realization of the best matches. Unlike in Karacan et al. (2014), which used E-type maps and average of quantile maps, using these 100 bundles created 100 different history match results from separate simulations, and distributions of results for in-place gas quantity, for example, from which uncertainty in coal property realizations could be evaluated. The study helped to determine the realization bundle that consisted of the spatial maps of coal properties, which resulted in minimum error. In addition, it was shown that both E-type and the average of realizations that gave the best match for invidual approximated the same properties resonably. Moreover, the determined realization bundle showed that the study field initially had 151.5 million m3 (cubic meter) of gas and 1.04 million m3 water in the coal, corresponding to Q90 of the entire range of probability for gas and close to Q75 for water. In 2013, in-place fluid amounts decreased to 138.9 million m3 and 0.997 million m3 for gas and water, respectively.