Explore the vast range of titles available.

Driving is a complex task concurrently drawing on multiple cognitive resources. Yet, there is a lack of studies investigating interactions at the brain-level among different driving subtasks in dual-tasking. This study investigates how visuospatial attentional demands related to increased driving difficulty interacts with different working memory load (WML) levels at the brain level. Using multichannel whole-head high density functional near-infrared spectroscopy (fNIRS) brain activation measurements, we aimed to predict driving difficulty level, both separate for each WML level and with a combined model. Participants drove for approximately 60 min on a highway with concurrent traffic in a virtual reality driving simulator. In half of the time, the course led through a construction site with reduced lane width, increasing visuospatial attentional demands. Concurrently, participants performed a modified version of the -back task with five different WML levels (from 0-back up to 4-back), forcing them to continuously update, memorize, and recall the sequence of the previous ' ' speed signs and adjust their speed accordingly. Using multivariate logistic ridge regression, we were able to correctly predict driving difficulty in 75.0% of the signal samples (1.955 Hz sampling rate) across 15 participants in an out-of-sample cross-validation of classifiers trained on fNIRS data separately for each WML level. There was a significant effect of the WML level on the driving difficulty prediction accuracies [range 62.2-87.1%; χ (4) = 19.9, < 0.001, Kruskal-Wallis test] with highest prediction rates at intermediate WML levels. On the contrary, training one classifier on fNIRS data across all WML levels severely degraded prediction performance (mean accuracy of 46.8%). Activation changes in the bilateral dorsal frontal (putative BA46), bilateral inferior parietal (putative BA39), and left superior parietal (putative BA7) areas were most predictive to increased driving difficulty. These discriminative patterns diminished at higher WML levels indicating that visuospatial attentional demands and WML involve interacting underlying brain processes. The changing pattern of driving difficulty related brain areas across WML levels could indicate potential changes in the multitasking strategy with level of WML demand, in line with the multiple resource theory.

AimTo undertake a comprehensive review of the best available evidence related to risk factors for child pedestrian motor vehicle collision (PMVC), as well as identification of established and emerging prevention strategies.MethodsArticles on risk factors were identified through a search of English language publications listed in Medline, Embase, Transport, SafetyLit, Web of Science, CINHAL, Scopus and PsycINFO within the last 30 years (~1989 onwards).ResultsThis state-of-the-art review uses the road safety Safe System approach as a new lens to examine three risk factor domains affecting child pedestrian safety (built environment, drivers and vehicles) and four cross-cutting critical issues (reliable collision and exposure data, evaluation of interventions, evidence-based policy and intersectoral collaboration).ConclusionsResearch conducted over the past 30 years has reported extensively on child PMVC risk factors. The challenge facing us now is how to move these findings into action and intervene to reduce the child PMVC injury and fatality rates worldwide.

Objective: The performance costs associated with cell phone use while driving were assessed meta-analytically using standardized measures of effect size along five dimensions. Background: There have been many studies on the impact of cell phone use on driving, showing some mixed findings. Methods: Twenty-three studies (contributing 47 analysis entries) met the appropriate conditions for the meta-analysis. The statistical results from each of these studies were converted into effect sizes and combined in the meta-analysis. Results: Overall, there were clear costs to driving performance when drivers were engaged in cell phone conversations. However, subsequent analyses indicated that these costs were borne primarily by reaction time tasks, with far smaller costs associated with tracking (lane-keeping) performance. Hands-free and handheld phones revealed similar patterns of results for both measures of performance. Conversation tasks tended to show greater costs than did information-processing tasks (e.g., word games). There was a similar pattern of results for passenger and remote (cell phone) conversations. Finally, there were some small differences between simulator and field studies, though both exhibited costs in performance for cell phone use. Conclusion: We suggest that (a) there are significant costs to driver reactions to external hazards or events associated with cell phone use, (b) hands-free cell phones do not eliminate or substantially reduce these costs, and (c) different research methodologies or performance measures may underestimate these costs. Application: Potential applications of this research include the assessment of performance costs attributable to different types of cell phones, cell phone conversations, experimental measures, or methodologies.

PurposeThis study has two aims: first, propose the use of the driver-pressure-state-impact-response (DPSIR) framework to expand the normal focus of impact pathways in social life cycle assessment (S-LCA) on endpoint impacts to a systematic analysis to find links between the main sources of social issues and impacts; second, develop a new impact assessment method to quantify the lifetime health and economic outcomes associated with social subcategories, for the first time, using decision analytic models.MethodsThe DPSIR framework is mapped to the corresponding elements of the S-LCA context in relation to the social subcategories defined in the UNEP/SETAC methodological sheets. Next, a more robust approach is developed for cause-impact chains between social subcategories and impacts on human well-being based on decision-analytic models (decision trees and Markov models) using healthcare approaches and data. Finally, the health and economic consequences associated with social subcategories are quantified by using Quality Adjusted Life Years (QALYs) and costs based on medical literature and healthcare studies.Results and discussionThe method was applied to the “working hours” social subcategory in Canada. The cause-effect chain is built using DPSIR framework in relation to the current social issue in Canada of working more than standard hours. Results of the decision analytic model show that working standard hours is more effective and cost-saving than working more than standard hours from the Canadian healthcare perspective. Working standard hours compared to more than standard hours led to an increase of 0.73 QALY and decrease in cost of $6702 per worker. Based on an estimated 2.4 million Canadian workers working more than standard hours, this resulted in a total gain of 1.7 million QALYs and saving of $16 billion overall. Using cost-effectiveness analysis, possible interventions at multiple entry points of the cause-effect chain within DPSIR framework are proposed to reduce the negative health impacts and associated costs of working more than standard hours in Canada.ConclusionsApplying the method on other subcategories could help decision-makers establish the cause-effect aspects of the social performance of their product systems using a quantitative systematic analysis from a life cycle perspective. This approach supports corporate decision-makers to quantify social impacts associated with their product supply chains by calculating QALYs and healthcare costs of their socio-economic conditions enabling them to identify possible interventions to improve the social performance.

Determining and assessing the links between human pressures and state-changes in marine and coastal ecosystems remains a challenge. Although there are several conceptual frameworks for describing these links, the DPSIR (Drivers – Pressures – State change – Impact – Response) framework has been widely adopted. Two possible reasons for this are: either the framework fulfils a major role, resulting from convergent evolution, or the framework is used often merely because it is used often, albeit uncritically. This comprehensive review, with lessons learned after two decades of use, shows that the approach is needed and there has been a convergent evolution in approach for coastal and marine ecosystem management. There are now 25 derivative schemes and a widespread and increasing usage of the DPSIR-type conceptual framework as a means of structuring and analyzing information in management and decision-making across ecosystems. However, there is less use of DPSIR in fully marine ecosystems and even this was mainly restricted to European literature. Around half of the studies are explicitly conceptual, not illustrating a solid case study. Despite its popularity since the early 1990s among the scientific community and the recommendation of several international institutions for its application, the framework has notable weaknesses to be addressed. These primarily relate to the long standing variation in interpretation (mainly between natural and social scientists) of the different components (particularly P, S and I) and to over-simplification of environmental problems such that cause-effect relationships cannot be adequately understood by treating the different DPSIR components as being mutually exclusive. More complex, nested, conceptual models and models with improved clarity are required to assess pressure-state change links in marine and coastal ecosystems. Our analysis shows that, because of its complexity, marine assessment and management constitutes a ’wicked problem’ and that there is an increasing need for a unifying approach, especially with the implementation of holistic regulations (e.g. European Directives). We emphasize the value of merging natural and social sciences and in showing similarities across human and natural environmental health. We show that previous approaches have adequately given conceptual and generic models but specificity and quantification is required.

As a subset of the human–environment system, the tourism ecosystem focuses on the complex dynamics and interactions between tourism activities and the natural environment. Among these, tourism ecological security (TES) is one of the core issues in the study of tourism ecosystems, aiming to balance economic development and ecological environment protection. Currently, the risk early warning of TES has not received widespread attention, and there is an urgent need for a tourism ecological safety risk early warning system to achieve TES monitoring, risk assessment, and decision support. Therefore, this study established a comprehensive TES evaluation system, systematically analyzed the evolution of TES in Sichuan Province from 2010 to 2022, and used the geographical detector to reveal the influencing factors and driving mechanisms of TES. Based on these achievements, an early risk warning system for TES was established based on the Bayesian network model, simulating the response of TES under single-variable and multi-variable scenarios. The research results reveal that TES changes with environmental changes, resource utilization and consumption, and the development of the tourism industry, and there are differences in the driving factors of TES under different conditions. There is a synergistic effect between the influencing factors of TES, and there is a threshold effect in the regulation of tourism ecological safety, revealing the efficiency and limitations of different regulatory strategies. The early risk warning model for TES based on the Bayesian network has high prediction accuracy and can provide effective support for the management and regulatory policies of TES.

Eye-tracking is an important approach to collect evidence regarding some participants’ driving fatigue. In this contribution, the authors present a non-intrusive system for evaluating driver fatigue by tracking eye movement behaviours. A real-time eye-tracker was used to monitor participants’ eye state for collecting eye-movement data. These data are useful to get insights into assessing participants’ fatigue state during monotonous driving. Ten healthy subjects performed continuous simulated driving for 1–2 h with eye state monitoring on a driving simulator in this study, and these measured features of the fixation time and the pupil area were recorded via using eye movement tracking device. For achieving a good cost-performance ratio and fast computation time, the fuzzy K-nearest neighbour was employed to evaluate and analyse the influence of different participants on the variations in the fixation duration and pupil area of drivers. The findings of this study indicated that there are significant differences in domain value distribution of the pupil area under the condition with normal and fatigue driving state. Result also suggests that the recognition accuracy by jackknife validation reaches to about 89% in average, implying that show a significant potential of real-time applicability of the proposed approach and is capable of detecting driver fatigue.

ObjectiveWe aim to place into the scientific literature information on the prevalence of operator fatigue as a factor in causing transportation mishaps, and the categories of improvements identified to address fatigue in transportation.MethodsWe analyzed the number of major National Transportation Safety Board (NTSB) investigations that identified fatigue as a probable cause, contributing factor, or a finding. We divided all NTSB recommendations addressing fatigue issued since the agency was founded into 7 subject categories, and placed each recommendation into the appropriate category. This information was then analyzed to determine the number of recommendations in each category, both overall and by transportation mode. Analysis was also performed regarding the types of organizations that received the recommendations, whether the recommended actions have been taken, and the NTSB's evaluation of whether the action taken satisfied a given recommendation.ResultsWe reviewed 182 major NTSB investigations completed between 1 January 2001 and 31 December 2012 and found that 20% of these investigations identified fatigue as a probable cause, contributing factor, or a finding. The presence of fatigue varied between among the modes of transportation, ranging from 40% of highway investigations to 4% of marine investigations. The first NTSB recommendation to address the safety risks associated with human fatigue was issued over 40 years ago, in 1972. Since then, the NTSB has issued 205 separate fatigue-specific recommendations. Scheduling policies and practices was the most common subject category accounting for 40% of all recommendations issued. Federal agencies received 54% of all recommendations, with 22% to transportation operators, and 16% to associations. Of all NTSB fatigue recommendations, 24% were open ranging from a low of 9% in highway to 39% in aviation. Overall, only 3% of open recommendations were classified “unacceptable,” whereas 16% of all closed recommendations were classified “unacceptable.”ConclusionsAlthough there has been over 100 years of progress in recognizing and addressing the safety risk posed by human fatigue in transportation, 20% of recent NTSB investigations have identified fatigue as a probable cause, contributing factor or finding. This analysis represents the first-ever examination of fatigue identified in major NTSB investigations across modes and of the focus, recipients, and classification status of fatigue-related safety recommendations. It demonstrates that fatigue remains a significant transportation safety risk.

Background The Pradhan Mantri Jan Arogya Yojana (PM-JAY), a publicly funded health insurance scheme, was launched in India in September 2018 to provide financial access to health services for poor Indians. PM-JAY design enables state-level program adaptations to facilitate implementation in a decentralized health implementation space. This study examines the competency, organizational, and leadership approaches affecting PM-JAY implementation in three contextually different Indian states. Methods We used a framework on implementation drivers (competency, organizational, and leadership) to understand factors facilitating or hampering implementation experiences in three PM-JAY models: third-party administrator in Uttar Pradesh, insurance in Chhattisgarh, and hybrid in Tamil Nadu. We adopted a qualitative exploratory approach and conducted 92 interviews with national, state, district, and hospital stakeholders involved in program design and implementation in Delhi, three state capitals, and two anonymized districts in each state, between February and April 2019. We used a deductive approach to content analysis and interpreted coded material to identify linkages between organizational features, drivers, and contextual elements affecting implementation. Results and conclusion PM-JAY guideline flexibilities enabled implementation in very different states through state-adapted implementation models. These models utilized contextually relevant adaptations for staff and facility competencies and organizational and facilitative administration, which had considerable scope for improvement in terms of recruitment, competency development, programmatic implementation support, and rationalizing the joint needs of the program and implementers. Adaptations also created structural barriers in staff interactions and challenged implicit power asymmetries and organizational culture, indicating a need for aligning staff hierarchies and incentive structures. At the same time, specific adaptations such as decentralizing staff selection and task shifting (all models); sharing of claims processing between the insurer and state agency (insurance and hybrid model); and using stringent empanelment, accreditation, monitoring, and benchmarking criteria for performance assessment, and reserving secondary care benefit packages for public hospitals (both in the hybrid model) contributed to successful implementation. Contextual elements such as institutional memory of previous schemes and underlying state capacities influenced all aspects of implementation, including leadership styles and autonomy. These variations make comparisons across models difficult, yet highlight constraints and opportunities for cross-learning and optimizing implementation to achieve universal health coverage in decentralized contexts.

•We examined combined THC/alcohol crash culpability in fatal car crashes.•Since 1991, a five-fold increase in combined THC/alcohol prevalence has occurred.•Each 0.01 BAC unit increased the culpability odds (COs) by approximately 9–11%.•Drivers who were positive for THC alone had 16% increased COs.•Combined THC/alcohol COs were greater than COs for alcohol or THC alone. Driving under the influence of alcohol or cannabis alone is associated with increased crash risk. This study explores the combined influence of low levels of alcohol (BAC≤0.08) and cannabis on crash risk. Drivers aged 20 years or older who had been tested for both drugs and alcohol after involvement in a fatal crash in the United States (1991–2008) were examined using a case–control design. Cases were drivers with at least one potentially unsafe driving action (UDA) recorded in relation to the crash (e.g., weaving); controls had none recorded. We examined the prevalence of driving under the influence of alcohol, cannabis, and both agents, for drivers involved in a fatal crash. Adjusted odds ratios of committing an UDA for alcohol alone, THC alone, and their combined effect were computed via logistic regression and adjusted for a number of potential confounders. Over the past two decades, the prevalence of THC and alcohol in car drivers involved in a fatal crash has increased approximately five-fold from below 2% in 1991 to above 10% in 2008. Each 0.01 BAC unit increased the odds of an UDA by approximately 9–11%. Drivers who were positive for THC alone had 16% increased odds of an UDA. When alcohol and THC were combined the odds of an UDA increased by approximately 8-10% for each 0.01 BAC unit increase over alcohol or THC alone. Drivers positive for both agents had greater odds of making an error than drivers positive for either alcohol or cannabis only. Further research is needed to better examine the interaction between cannabis concentration levels, alcohol, and driving. This research would support enforcement agencies and public health educators by highlighting the combined effect of cannabis at low BAC levels.