Speeding is well-established as a key contributor to crash risk and severity, yet it remains a common behaviour. Hence, speeding propensity is an important focus of study in road safety research. For pragmatic reasons, researchers investigating the causes and consequences of speeding behaviour, or evaluating interventions, will often need to measure speeding propensity indirectly, via self-report (e.g., in large-scale online studies). In the present study, we evaluated a number of self-report measures of driving behaviour. Specifically, we investigated several Driver Behaviour Questionnaire (DBQ) scales (All Violations, Ordinary Violations, Aggressive Violations, and Errors), as well as three speeding-specific measures, namely: (1) the DBQ Speed Items; (2) the Speed Scale from the Driving Style Questionnaire (DSQ); and (3) a novel measure of Relative Speed Choice. In the present study, we assessed the extent to which scores on these measures reflect actual risky speeding behaviour during real driving (as a construct closely related to speeding propensity). To achieve this, we used data from g-force triggered dashcams to estimate the frequency of young drivers' speed-related heavy-braking events (i.e., incidents in which the participant was exceeding the speed limit and then applied the brakes abruptly) over an average of 6.43 weeks. Among the standard DBQ scales, only Ordinary Violations scores were significantly associated with the frequency of speed-related heavy-braking events. However, compared with DBQ Ordinary Violations, scores on all three speeding-specific measures were better predictors of these events. Further, both the DSQ Speed Scale and the Relative Speed Choice measure provided better prediction than the DBQ Speed Items. We also found that prediction could be further improved (to a small degree) by using both the DSQ Speed Scale and the Relative Speed Choice measure in combination. These results suggest that the DBQ Ordinary Violations scale, the DBQ Speed Items, the DSQ Speed Scale, and the novel Relative Speed Choice measure can all potentially be used to assess speeding propensity in studies where drivers' risky speeding behaviour is of interest, but the latter two (alone or in combination) are the best options from a measurement perspective.

Physicians are required to move and manipulate equipment to achieve motor tasks such as surgical operations, endotracheal intubations, and intravenous cannulation. Understanding how movements are generated allows for the analysis of performance, skill development, and methods of teaching. Ecological-Dynamics (ECD) is a theoretical framework successfully utilized in sports to explain goal-directed actions and guide coaching and performance analysis via a Constraint-Led Approach (CLA). Its principles have been adopted by other domains including learning music and mathematics. Healthcare is yet to utilize ECD for analyzing and teaching practical skills. This article presents ECD theory and considers it as the approach to understand skilled behavior and developing training in medical skills.

Prospective gains and losses influence cognitive processing, but it is unresolved how they modulate flexible learning in changing environments. The prospect of gains might enhance flexible learning through prioritized processing of reward-predicting stimuli, but it is unclear how far this learning benefit extends when task demands increase. Similarly, experiencing losses might facilitate learning when they trigger attentional reorienting away from loss-inducing stimuli, but losses may also impair learning by increasing motivational costs or when negative outcomes are overgeneralized. To clarify these divergent views, we tested how varying magnitudes of gains and losses affect the flexible learning of feature values in environments that varied attentional load by increasing the number of interfering object features. With this task design, we found that larger prospective gains improved learning efficacy and learning speed, but only when attentional load was low. In contrast, expecting losses impaired learning efficacy, and this impairment was larger at higher attentional load. These findings functionally dissociate the contributions of gains and losses on flexible learning, suggesting they operate via separate control mechanisms. One mechanism is triggered by experiencing loss and reduces the ability to reduce distractor interference, impairs assigning credit to specific loss-inducing features, and decreases efficient exploration during learning. The second mechanism is triggered by experiencing gains, which enhances prioritizing reward-predicting stimulus features as long as the interference of distracting features is limited. Taken together, these results support a rational theory of cognitive control during learning, suggesting that experiencing losses and experiencing distractor interference impose costs for learning.

Insulin is a high-risk medicine that has been implicated in serious adverse events for hospital inpatients, including medication-error related deaths. Most insulin errors occur during administration, and "wrong dose" is the most common type. A paper-based subcutaneous insulin chart (the "NSIC") was developed for the Australian Commission on Safety and Quality in Health Care, using a range of human factors methods, with the aim of reducing the opportunity for errors. The present lab-based study empirically assessed whether the NSIC's human factors design translates into improved user-performance in the determination of insulin doses, compared with a pre-existing chart. Forty-one experienced nurses and 48 novice chart-users completed 60 experimental trials (30 per chart), in which they determined doses to administer to patients. Both groups determined insulin doses faster, and made fewer dose errors, when using the NSIC. These results support the utility of the usability heuristics employed in developing the chart.

Phenylketonuria (PKU) is a genetic disorder characterized by impaired ability to metabolize the amino acid phenylalanine (Phe) into tyrosine (Tyr), a precursor to neurotransmitters including dopamine. Dopamine deficiency is proposed to underlie cognitive deficits and reduced contrast sensitivity in PKU. However, consensus has not been reached on the spatial frequencies impacted. Conflicting results may be due to use of a chart-based contrast sensitivity test with poor test-retest reliability or to inclusion of participants with varied blood levels of Phe/Tyr. To assess these possibilities, we used a more rigorous, computer-generated psychophysical test to measure contrast sensitivity in 14 children with PKU (M age = 11.6 years), who had been treated with a phenylalanine-restrictive diet from an early age, and 81 age-matched controls (M age = 11.9 years). Seven children also received sapropterin dihydrochloride (Kuvan®; a drug that may decrease Phe) at some point during the study. Contrast sensitivity was measured with a four-alternative forced-choice orientation discrimination task (Freiburg Visual Acuity Test) at five spatial frequencies (1.5 - 18.0 cpd). A subset of children (9 PKU; 10 control) returned for repeat assessments; four children were receiving Kuvan. Phe/Tyr levels were measured in children with PKU before each assessment. The PKU group (before Kuvan treatment) showed significantly lower contrast sensitivity (47%) at 1.5 cpd compared to controls; no group differences were found at higher spatial frequencies. Kuvan-treated children showed a reduction in Phe/Tyr, and a resolution of the contrast sensitivity deficit. The deficit persisted in children treated with diet alone, although a small improvement was noted. The contrast sensitivity deficit at low spatial frequencies in PKU is consistent with some previous studies, but the lack of deficit at higher spatial frequencies is not. This deficit is dependent on blood levels of Phe/Tyr, and possibly on the choice of contrast sensitivity test.

We previously found that a six-session online hazard perception training course, which incorporates evidencebased learning strategies and footage of over a hundred real crashes, improved hazard perception skill and reduced risk-taking intentions in novice drivers who had passed their on-road driving test within the previous three years. However, one issue with targeting crash-prevention training at individuals who are already driving unsupervised is that drivers are at their highest crash risk immediately after they pass their on-road driving test. That is, the training may arrive too late to protect drivers while they are at their most vulnerable. It is also possible that it may prove difficult to persuade drivers to complete an unsupervised training course if they are already licensed to drive independently. Given that learner drivers cannot drive unsupervised, and that they are typically supervised by a parent, one potential strategy is to target the training at learners and to ask their parents to provide one-on-one mentoring throughout the course. We therefore recruited learner driver/parent-supervisor dyads to participate in a randomized control study, with the objective of examining the effects of the hazard perception training course on aspects of driving behaviour associated with crash risk (as measured using validated computer-based tests). Outcome measures included two hazard perception skill assessments (a response time hazard perception test and a verbal response hazard prediction test), and three tests assessing aspects of risktaking propensity in driving (speed choice, following distance, and gap acceptance). Learners who completed the course (N = 26) significantly improved their scores on both hazard perception skill measures, and also chose safer following distances, compared with a waitlist control group (N = 23). However, the training did not significantly reduce learners' speed choice or gap acceptance propensity. The hazard perception skill of parentsupervisors, who observed the course but did not complete it, also improved on both hazard perception measures, relative to controls. Additionally, both learners and their parent-supervisors reported a range of positive effects on the learners' real-world driving performance. These results suggest that this type of hazard perception training could be beneficial if deployed during the learner phase of driver licensing.

A key goal of driver training is to teach drivers to avoid crashes. However, in traditional driver training, drivers are unlikely to see even a single example of the class of event that we want them to learn to avoid. We developed a six-session automated online hazard perception training course for drivers, which incorporates a range of evidence-based strategies and employs extensive video footage of real crashes. We evaluated this course in a randomized control trial by examining its effects on previously-validated computer-based measures of hazard perception, hazard prediction, speed choice, following distance, and gap acceptance propensity, as well as self-rated measures of driver skill, safety, and real world transfer. We found that the course resulted in significant improvements in hazard perception response time and hazard prediction scores, and significantly longer vehicle following distances. Additionally, all participants in the trained group reported that their real world driving behaviour had improved. No significant training effects were found for the other measures. The results suggest that the course can improve key behaviours associated with crash risk.

In feature learning, uncertainty about feature values is reduced. Selective attention can help this, implying that agents should focus attention more under greater expected uncertainty about action outcomes. Little work tests this “attention-for-learning” prediction, and in particular it is unknown whether attention-for-learning is sensitive to the degree to which uncertainty can actually be reduced. Here we tested the attention-for-learning hypothesis in a naturalistic learning task that manipulated both reducible and irreducible forms of uncertainty, and quantified the strength of selective attention using attention-augmented reinforcement learning (RL) models. Human participants performed a 2-AFC object selection task in which multidimensional objects with a particular feature were more likely to be rewarded. Reducible uncertainty was manipulated between blocks by having objects vary along either two or five possible feature dimensions (different arms, body shapes, patterns, textures, or colors). Irreducible uncertainty took the form of different reward probabilities, either 0.70 or 0.85. As expected, when either form of uncertainty was higher, response times were longer, learning was slower, and asymptotic performance was lower. On blocks where one form of uncertainty was high and the other was low, these performance measures did not differ. However model results show that this similar performance was the result of different mechanisms. Specifically, when reducible uncertainty was high and irreducible uncertainty was low, participants had narrower attentional focus and greater exploratory biases than in the opposite condition. These results demonstrate that attention flexibly adjusts to the specific type of decision uncertainty. When faced with high levels of reducible uncertainty, attention becomes more focused and exploration increases, but the reverse is true for irreducible uncertainty, even when the resulting behaviour is highly similar. Taken together, these findings provide quantitative evidence for flexible adjustment of attention during learning to specific types of experienced uncertainty.

Abstract Aims To identify the potential sources of inaccuracy in manually measured adult respiratory rate (RR) data and quantify their effects. Design Quantitative systematic review with meta‐analyses where appropriate. Data Sources Medline, CINAHL, and Cochrane Library (from database inception to 31 July 2019). Review Methods Studies presenting data on individual sources of inaccuracy in the manual measurement of adult RR were analysed, assessed for quality, and grouped according to the source of inaccuracy investigated. Quantitative data were extracted and synthesized and meta‐analyses performed where appropriate. Results Included studies (N = 49) identified five sources of inaccuracy. The awareness effect creates an artefactual reduction in actual RR, and observation methods involving shorter counts cause systematic underscoring. Individual RR measurements can differ substantially in either direction between observations due to inter‐ or intra‐observer variability. Value bias, where particular RRs are over‐represented (suggesting estimation), is a widespread problem. Recording omission is also widespread, with higher average rates in inpatient versus triage/admission contexts. Conclusion This review demonstrates that manually measured RR data are subject to several potential sources of inaccuracy. Impact RR is an important indicator of clinical deterioration and commonly included in track‐and‐trigger systems. However, the usefulness of RR data depends on the accuracy of the observations and documentation, which are subject to five potential sources of inaccuracy identified in this review. A single measurement may be affected by several factors. Hence, clinicians should interpret recorded RR data cautiously unless systems are in place to ensure its accuracy. For nurses, this includes counting rather than estimating RRs, employing 60‐s counts whenever possible, ensuring patients are unaware that their RR is being measured, and documenting the resulting value. For any given site, interventions to improve measurement should take into account the local organizational and cultural context, available resources, and the specific measurement issues that need to be addressed.

Saccade detection is a critical step in the analysis of gaze data. A common method for saccade detection is to use a simple threshold for velocity or acceleration values, which is typically estimated from the data using the mean and standard deviation. However, this method has the downside of being influenced by the very signal it is trying to detect, the outlying velocities or accelerations that occur during saccades. We propose instead to use the median absolute deviation (MAD), a robust estimator of the standard deviation that is not influenced by outliers. We modify an algorithm proposed by Nyström and colleagues, and quantify saccade detection performance in both simulated and human data. Our modified algorithm shows a significant and marked improvement in saccade detection, showing both more true positives and less false negatives. We conclude that robust estimators can be widely adopted in other common, automatic gaze classification algorithms due to their ease of implementation.

Human-Autonomy Teaming (HAT) is of growing interest in the military sector, particularly in its application to war gaming using semi-automated computer generated forces (CGF). In these applications, one or more operators manage multiple semi-autonomous game entities. If effective collaboration (teaming) is to occur between operators and entities, then having effective interaction models is essential if the levels of trust and explanatory capability required for military operations are to be delivered. The Situation Awareness-Based Agent Transparency (SAT) Model has been identified as providing a suitable conceptual framework for such models. However, while the SAT model is informed by the Belief-Desire-Intention (BDI) model of agency, to date there has been no implementation of an interaction model at the level of desires and intentions, i.e. goals. In this paper, we propose that GORITE, a novel BDI framework that employs explicit goal representations and a shared data context for goal execution, provides a suitable platform for the development of SAT-enabled agents. The feasibility of this proposition is demonstrated through the development of a simple but representative CGF case study.

Particular design features intended to improve usability - including graphically displayed observations and integrated colour-based scoring-systems have been shown to increase the speed and accuracy with which users of hospital observation charts detect abnormal patient observations. We used eye-tracking to evaluate two potential cognitive mechanisms underlying these effects. Novice chart-users completed a series of experimental trials in which they viewed patient data presented on one of three observation chart designs (varied within subjects), and indicated which observation was abnormal (or that none were). A chart that incorporated both graphically displayed observations and an integrated colour-based scoring-system yielded faster, more accurate responses and fewer, shorter fixations than a graphical chart without a colour-based scoring-system. The latter, in turn, yielded the same advantages over a tabular chart (which incorporated neither design feature). These results suggest that both colour-based scoring-systems and graphically displayed observations improve search efficiency and reduce the cognitive resources required to process vital sign data.

The Belief-Desire-Intention (BDI) model of agency has been a popular choice for the modelling of goal-based behaviour for both individual agents and more recently, teams of agents. Numerous frameworks have been developed since the model was first proposed in the early 1980s. However, while the more recent frameworks support a delegative model of agent/agent and human/agent collaboration, no frameworks support a general model of collaboration. Given the importance of collaboration in the development of practical semi-autonomous agent applications, we consider this to constitute a major limitation of traditional BDI frameworks. In this paper, we present GORITE, a novel BDI framework that by employing explicit goal representations, overcomes many of the limitations of traditional frameworks. In terms of human/agent collaboration, key requirements are identified and through the use of a representative but simple example, the ability of GORITE to address those requirements is demonstrated.

Computer-based hazard perception tests are used in a number of countries as part of the driver licensing processes, and hence evaluating the validity of such tests is crucial. One strategy for assessing the validity of the scores generated by a hazard perception test is to determine whether they can predict on-road driving performance. Only a few prior studies have attempted this, all relying on the subjective ratings of an examiner who was present during a single brief drive and was not blind to the driver's demographic characteristics, potentially contaminating the outcomes. Additionally, only one such study focused on the most relevant participant group with respect to the validity of tests used in licencing processes, namely young drivers. We sought to remedy this situation in the present project by measuring young drivers' performance over an extended period of everyday driving via g-force triggered video cameras ("dashcams") installed in their own vehicles. As a precursor to the dashcam study itself, we developed a new computerized hazard perception test and assessed the validity of its scores by more traditional means (Study 1). As expected, test scores distinguished between high-risk and lower-risk driver groups, and correlated with scores on an established hazard perception test previously shown to predict crash risk. In the subsequent dashcam study (Study 2), the frequency of heavy-braking events (controlling for distance driven) was used as a more objective measure of driving performance. Results indicated that drivers with higher rates of heavy braking had slower hazard perception response times, further supporting the use of these scores as a valid measure of drivers' ability to exercise hazard perception skill during real driving. More generally, this study also demonstrates the viability of using low-cost off-the-shelf dashcams to measure real-world driving behaviour.

AIMS AND OBJECTIVES:To investigate whether awareness of manual respiratory rate monitoring affects respiratory rate in adults, and whether count duration influences respiratory rate estimates.BACKGROUND:Nursing textbooks typically suggest that the patient should ideally be unaware of respiratory rate observations; however, there is little published evidence of the effect of awareness on respiratory rate, and none specific to manual measurement. In addition, recommendations about the length of the respiratory rate count vary from text to text, and the relevant empirical evidence is scant, inconsistent and subject to substantial methodological limitations.DESIGN:Experimental study with awareness of respiration monitoring (aware, unaware; randomised between-subjects) and count duration (60 s, 30 s, 15 s; within-subjects) as the independent variables. Respiratory rate (breaths/minute) was the dependent variable.METHODS:Eighty-two adult volunteers were randomly assigned to aware and unaware conditions. In the baseline block, no live monitoring occurred. In the subsequent experimental block, the researcher informed aware participants that their respiratory rate would be counted, and did so. Respirations were captured throughout via video recording, and counted by blind raters viewing 60-, 30- and 15-s extracts. The data were collected in 2015.RESULTS:There was no baseline difference between the groups. During the experimental block, the respiratory rates of participants in the aware condition were an average of 2.13 breaths/minute lower compared to unaware participants. Reducing the count duration from 1 min to 15 s caused respiratory rate to be underestimated by an average of 2.19 breaths/minute (and 0.95 breaths/minute for 30-s counts). The awareness effect did not depend on count duration.CONCLUSIONS:Awareness of monitoring appears to reduce respiratory rate, and shorter monitoring durations yield systematically lower respiratory rate estimates.RELEVANCE TO CLINICAL PRACTICE:When interpreting and acting upon respiratory rate data, clinicians should consider the potential influence of these factors, including cumulative effects.

Sakata, Shinichiro; Grove, Phillip; Watson, Marcus; Stevenson, Andrew Author Information

More number of simulations in healthcare currently focuses on simulation-based education (SBE); however, the application of simulation has greater potential to address safety and quality in healthcare. This chapter covers framing simulation into micro, meso and macro levels of analysis for healthcare systems and processes. Using a framework to scaffold different simulations may help to bridge the divide between training and design at all levels of healthcare. The use of discrete event simulation to improve macro processes of care should be achievable by most simulation providers. Nevertheless, as technology continues to grow exponentially, the healthcare simulation community needs to develop the capacity to conduct predictive simulation to understand how to adapt to new technologies and, even more importantly, to help design the right technologies, processes and training to ensure safe, high-quality care. Examples are used to illustrate the application of diagnostic, predictive and interventional simulations at the micro, meso and macro levels to improve healthcare outcomes.