Leadership and teamwork in medical emergencies: Performance of nursing students and registered nurses in simulated patient scenarios

Aims and objectives: To examine nursing students' and registered nurses' teamwork skills whilst managing simulated deteriorating patients. Background: Studies continue to show the lack of timely recognition of patient deterioration. Management of deteriorating patients can be influenced by education and experience. Design: Mixed methods study conducted in two universities and a rural hospital in Victoria, and one university in Queensland, Australia. Methods: Three simulation scenarios (chest pain, hypovolaemic shock and respiratory distress) were completed in teams of three by 97 nursing students and 44 registered nurses, equating to a total of 32 student and 15 registered nurse teams. Data were obtained from (1) Objective Structured Clinical Examination rating to assess performance; (2) Team Emergency Assessment Measure scores to assess teamwork; (3) simulation video footage; (4) reflective interview during participants' review of video footage. Qualitative thematic analysis of video and interview data was undertaken. Results: Objective structured clinical examination performance was similar across registered nurses and students (mean 54% and 49%); however, Team Emergency Assessment Measure scores differed significantly between the two groups (57% vs 38%, t = 6·841, p < 0·01). In both groups, there was a correlation between technical (Objective Structured Clinical Examination) and nontechnical (Team Emergency Assessment Measure) scores for the respiratory distress scenario (student teams: r = 0·530, p = 0·004, registered nurse teams r = 0·903, p < 0·01) and hypovolaemia scenario (student teams: r = 0·534, p = 0·02, registered nurse teams: r = 0·535, p = 0·049). Themes generated from the analysis of the combined quantitative and qualitative data were as follows: (1) leadership and followership behaviours; (2) help-seeking behaviours; (3) reliance on previous experience; (4) fixation on a single detail; and (5) team support. Conclusions: There is scope to improve leadership, team work and task management skills for registered nurses and nursing students. Simulation appears to be beneficial in enabling less experienced staff to assess their teamwork skills. Relevance to clinical practice: There is a need to encourage less experienced staff to become leaders and for all staff to develop improved teamwork skills for medical emergencies.

Effect of heat on firefighters' work performance and physiology

Wildland firefighters often perform their duties under both hot and mild ambient temperatures. However, the direct impact of different ambient temperatures on firefighters' work performance has not been quantified. This study compared firefighters' work performance and physiology during simulated wildland firefighting work in hot (HOT; 32°C, 43% RH) and temperate (CON; 19°C, 56% RH) conditions. Firefighters (n=38), matched and allocated to either the CON (n=18) or HOT (n=20) condition, performed simulated self-paced wildland fire suppression tasks (e.g., hose rolling/dragging, raking) in firefighting clothing for six hours, separated by dedicated rest breaks. Task repetitions were counted (and converted to distance or area). Core temperature (Tc), skin temperature (Tsk), and heart rate were recorded continuously throughout the protocol. Urine output was measured before and during the protocol, and urine specific gravity (USG) analysed, to estimate hydration. Ad libitum fluid intake was also recorded. There were no differences in overall work output between conditions for any physical task. Heart rate was higher in the HOT (55±2% HRmax) compared to the CON condition (51±2% HRmax) for the rest periods between bouts, and for the static hose hold task (69±3% HRmax versus 65±3% HRmax). Tc and Tsk were 0.3±0.1°C and 3.1±0.2°C higher in the HOT compared to the CON trial. Both pre- and within- shift fluid intake were increased two-fold in the heat, and participants in the heat recorded lower USG results than their CON counterparts. There was no difference between the CON and HOT conditions in terms of their work performance, and firefighters in both experimental groups increased their work output over the course of the simulated shift. Though significantly hotter, participants in the heat also managed to avoid excessive cardiovascular and thermal strain, likely aided by the frequent rest breaks in the protocol, and through doubling their fluid intake. Therefore, it can be concluded that wildland firefighters are able to safely and efficiently perform their duties under hot conditions, at least over six hours.

Performance analysis and prediction in triathlon

Performance in triathlon is dependent upon factors that include somatotype, physiological capacity, technical proficiency and race strategy. Given the multidisciplinary nature of triathlon and the interaction between each of the three race components, the identification of target split times that can be used to inform the design of training plans and race pacing strategies is a complex task. The present study uses machine learning techniques to analyse a large database of performances in Olympic distance triathlons (2008–2012). The analysis reveals patterns of performance in five components of triathlon (three race “legs” and two transitions) and the complex relationships between performance in each component and overall performance in a race. The results provide three perspectives on the relationship between performance in each component of triathlon and the final placing in a race. These perspectives allow the identification of target split times that are required to achieve a certain final place in a race and the opportunity to make evidence-based decisions about race tactics in order to optimise performance.

Multiple days of heat exposure on firefighters' work performance and physiology

This study assessed the accumulated effect of ambient heat on the performance of, and physiological and perceptual responses to, intermittent, simulated wildfire fighting tasks over three consecutive days. Firefighters (n = 36) were matched and allocated to either the CON (19°C) or HOT (33°C) condition. They performed three days of intermittent, self-paced simulated firefighting work, interspersed with physiological testing. Task repetitions were counted (and converted to distance or area) to determine work performance. Participants were asked to rate their perceived exertion and thermal sensation after each task. Heart rate, core temperature (Tc), and skin temperature (Tsk) were recorded continuously throughout the simulation. Fluids were consumed ad libitum. Urine volume was measured throughout, and urine specific gravity (USG) analysed, to estimate hydration. All food and fluid consumption was recorded. There was no difference in work output between experimental conditions. However, significant variation in performance responses between individuals was observed. All measures of thermal stress were elevated in the HOT, with core and skin temperature reaching, on average, 0.24 ± 0.08°C and 2.81 ± 0.20°C higher than the CON group. Participants' doubled their fluid intake in the HOT condition, and this was reflected in the USG scores, where the HOT participants reported significantly lower values. Heart rate was comparable between conditions at nearly all time points, however the peak heart rate reached each circuit was 7 ± 3% higher in the CON trial. Likewise, RPE was slightly elevated in the CON trial for the majority of tasks. Participants' work output was comparable between the CON and HOT conditions, however the performance change over time varied significantly between individuals. It is likely that the increased fluid replacement in the heat, in concert with frequent rest breaks and task rotation, assisted with the regulation of physiological responses (e.g., heart rate, core temperature).

Oxytocin enhances the appropriate use of human social cues by the domestic dog (Canis familiaris) in an object choice task

It has been postulated that the neuropeptide, oxytocin, is involved in human-dog bonding. This may explain why dogs, compared to wolves, are such good performers on object choice tasks, which test their ability to attend to, and use, human social cues in order to find hidden food treats. The objective of this study was to investigate the effect of intranasal oxytocin administration, which is known to increase social cognition in humans, on domestic dogs' ability to perform such a task. We hypothesised that dogs would perform better on the task after an intranasal treatment of oxytocin. Sixty-two (31 males and 31 females) pet dogs completed the experiment over two different testing sessions, 5-15 days apart. Intranasal oxytocin or a saline control was administered 45 min before each session. All dogs received both treatments in a pseudo-randomised, counterbalanced order. Data were collected as scores out of ten for each of the four blocks of trials in each session. Two blocks of trials were conducted using a momentary distal pointing cue and two using a gazing cue, given by the experimenter. Oxytocin enhanced performance using momentary distal pointing cues, and this enhanced level of performance was maintained over 5-15 days time in the absence of oxytocin. Oxytocin also decreased aversion to gazing cues, in that performance was below chance levels after saline administration but at chance levels after oxytocin administration.

Modeling intermittent cycling performance in hypoxia using the critical power concept

PURPOSE: This study investigated the efficacy of an intermittent critical power model, termed the "work-balance" (W'BAL) model, during high-intensity exercise in hypoxia. METHODS: Eleven trained, male cyclists (mean ± SD; age 27 ± 6.6 yr, V[Combining Dot Above]O2peak 4.79 ± 0.56 L.min) completed a maximal ramp test and a 3 min "all-out" test to determine critical power (CP) and work performed above CP (W'). On another day an intermittent exercise test to task failure was performed. All procedures were performed in normoxia (NORM) and hypoxia (HYPO; FiO2 ≈ 0.155) in a single-blind, randomized and counter-balanced experimental design. The W'BAL model was used to calculate the minimum W' (W'BALmin) achieved during the intermittent test. W'BALmin in HYPO was also calculated using CP + W' derived in NORM (N+H). RESULTS: In HYPO there was an 18% decrease in V[Combining Dot Above]O2peak (4.79 ± 0.56 vs 3.93 ± 0.47 L.min ; P<0.001) and a 9% decrease in CP (347 ± 45 vs 316 ± 46 W; P<0.001). No significant change for W' occurred (13.4 ± 3.9 vs 13.7 ± 4.9 kJ; P=0.69; NORM vs HYPO). The change in V[Combining Dot Above]O2peak was significantly correlated with the change in CP (r = 0.72; P=0.01). There was no difference between NORM and HYPO for W'BALmin (1.1 ± 0.9 kJ vs 1.2 ± 0.6 kJ). The N+H analysis grossly overestimated W'BALmin (7.8 ± 3.4 kJ) compared with HYPO (P<0.001). CONCLUSION: The W'BAL model produced similar results in hypoxia and normoxia, but only when model parameters were determined under the same environmental conditions as the performance task. Application of the W'BAL model at altitude requires a modification of the model, or that CP and W' are measured at altitude.

Performance analysis for object-oriented software: a systematic mapping

Performance is a crucial attribute for most software, making performance analysis an important software engineering task. The difficulty is that modern applications are challenging to analyse for performance. Many profiling techniques used in real-world software development struggle to provide useful results when applied to large-scale object-oriented applications. There is a substantial body of research into software performance generally but currently there exists no survey of this research that would help identify approaches useful for object-oriented software. To provide such a review we performed a systematic mapping study of empirical performance analysis approaches that are applicable to object-oriented software. Using keyword searches against leading software engineering research databases and manual searches of relevant venues we identified over 5,000 related articles published since January 2000. From these we systematically selected 253 applicable articles and categorised them according to ten facets that capture the intent, implementation and evaluation of the approaches. Our mapping study results allow us to highlight the main contributions of the existing literature and identify areas where there are interesting opportunities. We also find that, despite the research including approaches specifically aimed at object-oriented software, there are significant challenges in providing actionable feedback on the performance of large-scale object-oriented applications.

StressCloud: a tool for analysing performance and energy consumption of cloud applications

Finding the best deployment configuration that maximises energy efficiency while guaranteeing system performance of cloud applications is an extremely challenging task. It requires the evaluation of system performance and energy consumption under a wide variety of realistic workloads and deployment configurations. This paper demonstrates StressCloud, an automatic performance and energy consumption analysis tool for cloud applications in real-world cloud environments. StressCloud supports 1) the modelling of realistic cloud application workloads, 2) the automatic generation and running of load tests, and 3) the profiling of system performance and energy consumption. A demonstration video can be accessed at: https://www.youtube.com/watch?v=0l4-a-CNtVQ.

Prosthetic motor imaginary task classification using single channel of electroencephalography

Brain Computer Interface (BCI) is playing a very important role in human machine communications. Recent communication systems depend on the brain signals for communication. In these systems, users clearly manipulate their brain activity rather than using motor movements in order to generate signals that could be used to give commands and control any communication devices, robots or computers. In this paper, the aim was to estimate the performance of a brain computer interface (BCI) system by detecting the prosthetic motor imaginary tasks by using only a single channel of electroencephalography (EEG). The participant is asked to imagine moving his arm up or down and our system detects the movement based on the participant brain signal. Some features are extracted from the brain signal using Mel-Frequency Cepstrum Coefficient and based on these feature a Hidden Markov model is used to help in knowing if the participant imagined moving up or down. The major advantage in our method is that only one channel is needed to take the decision. Moreover, the method is online which means that it can give the decision as soon as the signal is given to the system. Hundred signals were used for testing, on average 89 % of the up down prosthetic motor imaginary tasks were detected correctly. This method can be used in many different applications such as: moving artificial prosthetic limbs and wheelchairs due to it's high speed and accuracy.

Prosthetic motor imaginary task classification based on EEG quality assessment features

Brain Computer Interface (BCI) plays an important role in the communication between human and machines. This communication is based on the human brain signals. In these systems, users use their brain instead of the limbs or body movements to do tasks. The brain signals are analyzed and translated into commands to control any communication devices, robots or computers. In this paper, the aim was to enhance the performance of a brain computer interface (BCI) systems through better prosthetic motor imaginary tasks classification. The challenging part is to use only a single channel of electroencephalography (EEG). Arm movement imagination is the task of the user, where (s)he was asked to imagine moving his arm up or down. Our system detected the imagination based on the input brain signal. Some EEG quality features were extracted from the brain signal, and the Decision Tree was used to classify the participant's imagination based on the extracted features. Our system is online which means that it can give the decision as soon as the signal is given to the system (takes only 20 ms). Also, only one EEG channel is used for classification which reduces the complexity of the system which leads to fast performance. Hundred signals were used for testing, on average 97.4% of the up-down prosthetic motor imaginary tasks were detected correctly. This method can be used in many different applications such as: moving artificial limbs and wheelchairs due to it's high speed and accuracy.

Prediciton of emergency events: a multi-task multi-label learning approach

Prediction of patient outcomes is critical to plan resources in an hospital emergency department. We present a method to exploit longitudinal data from Electronic Medical Records (EMR), whilst exploiting multiple patient outcomes. We divide the EMR data into segments where each segment is a task, and all tasks are associated with multiple patient outcomes over a 3, 6 and 12 month period. We propose a model that learns a prediction function for each task-label pair, interacting through two subspaces: the first subspace is used to impose sharing across all tasks for a given label. The second subspace captures the task-specific variations and is shared across all the labels for a given task. The proposed model is formulated as an iterative optimization problems and solved using a scalable and efficient Block co-ordinate descent (BCD) method. We apply the proposed model on two hospital cohorts - Cancer and Acute Myocardial Infarction (AMI) patients collected over a two year period from a large hospital emergency department. We show that the predictive performance of our proposed models is significantly better than those of several state-of-the-art multi-task and multi-label learning methods.

A new transfer learning framework with application to model-agnostic multi-task learning

Learning from small number of examples is a challenging problem in machine learning. An effective way to improve the performance is through exploiting knowledge from other related tasks. Multi-task learning (MTL) is one such useful paradigm that aims to improve the performance through jointly modeling multiple related tasks. Although there exist numerous classification or regression models in machine learning literature, most of the MTL models are built around ridge or logistic regression. There exist some limited works, which propose multi-task extension of techniques such as support vector machine, Gaussian processes. However, all these MTL models are tied to specific classification or regression algorithms and there is no single MTL algorithm that can be used at a meta level for any given learning algorithm. Addressing this problem, we propose a generic, model-agnostic joint modeling framework that can take any classification or regression algorithm of a practitioner’s choice (standard or custom-built) and build its MTL variant. The key observation that drives our framework is that due to small number of examples, the estimates of task parameters are usually poor, and we show that this leads to an under-estimation of task relatedness between any two tasks with high probability. We derive an algorithm that brings the tasks closer to their true relatedness by improving the estimates of task parameters. This is achieved by appropriate sharing of data across tasks. We provide the detail theoretical underpinning of the algorithm. Through our experiments with both synthetic and real datasets, we demonstrate that the multi-task variants of several classifiers/regressors (logistic regression, support vector machine, K-nearest neighbor, Random Forest, ridge regression, support vector regression) convincingly outperform their single-task counterparts. We also show that the proposed model performs comparable or better than many state-of-the-art MTL and transfer learning baselines.

What shall i share and with whom? A multi-task learning formulation using multi-faceted task relationships

Multi-task learning is a learning paradigm that improves the performance of "related" tasks through their joint learning. To do this each task answers the question "Which other task should I share with"? This task relatedness can be complex - a task may be related to one set of tasks based on one subset of features and to other tasks based on other subsets. Existing multi-task learning methods do not explicitly model this reality, learning a single-faceted task relationship over all the features. This degrades performance by forcing a task to become similar to other tasks even on their unrelated features. Addressing this gap, we propose a novel multi-task learning model that leams multi-faceted task relationship, allowing tasks to collaborate differentially on different feature subsets. This is achieved by simultaneously learning a low dimensional sub-space for task parameters and inducing task groups over each latent subspace basis using a novel combination of L1 and pairwise L∞ norms. Further, our model can induce grouping across both positively and negatively related tasks, which helps towards exploiting knowledge from all types of related tasks. We validate our model on two synthetic and five real datasets, and show significant performance improvements over several state-of-the-art multi-task learning techniques. Thus our model effectively answers for each task: What shall I share and with whom?

Effects of low- versus high-fidelity simulations on the cognitive burden and performance of entry-level paramedicine students: a mixed-methods comparison trial using eye-tracking, continuous heart rate, difficulty rating scales, video observation and inte

INTRODUCTION: High-fidelity simulation-based training is often avoided for early-stage students because of the assumption that while practicing newly learned skills, they are ill suited to processing multiple demands, which can lead to "cognitive overload" and poorer learning outcomes. We tested this assumption using a mixed-methods experimental design manipulating psychological immersion. METHODS: Thirty-nine randomly assigned first-year paramedicine students completed low- or high-environmental fidelity simulations [low-environmental fidelity simulations (LFenS) vs. high-environmental fidelity simulation (HFenS)] involving a manikin with obstructed airway (SimMan3G). Psychological immersion and cognitive burden were determined via continuous heart rate, eye tracking, self-report questionnaire (National Aeronautics and Space Administration Task Load Index), independent observation, and postsimulation interviews. Performance was assessed by successful location of obstruction and time-to-termination. RESULTS: Eye tracking confirmed that students attended to multiple, concurrent stimuli in HFenS and interviews consistently suggested that they experienced greater psychological immersion and cognitive burden than their LFenS counterparts. This was confirmed by significantly higher mean heart rate (P < 0.001) and National Aeronautics and Space Administration Task Load Index mental demand (P < 0.05). Although group allocation did not influence the proportion of students who ultimately revived the patient (58% vs. 30%, P < 0.10), the HFenS students did so significantly more quickly (P < 0.01). The LFenS students had low immersion resulting in greater assessment anxiety. CONCLUSIONS: High-environmental fidelity simulation engendered immersion and a sense of urgency in students, whereas LFenS created assessment anxiety and slower performance. We conclude that once early-stage students have learned the basics of a clinical skill, throwing them in the "deep end" of high-fidelity simulation creates significant additional cognitive burden but this has considerable educational merit.

Optimal task placement with QoS constraints in geo-distributed data centers using DVFS

 With the rising demands on cloud services, the electricity consumption has been increasing drastically as the main operational expenditure (OPEX) to data center providers. The geographical heterogeneity of electricity prices motivates us to study the task placement problem over geo-distributed data centers. We exploit the dynamic frequency scaling technique and formulate an optimization problem that minimizes OPEX while guaranteeing the quality-of-service, i.e., the expected response time of tasks. Furthermore, an optimal solution is discovered for this formulated problem. The experimental results show that our proposal achieves much higher cost-efficiency than the traditional resizing scheme, i.e., by activating/deactivating certain servers in data centers.