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This study aims to assess the frequency and associated factors of surgical "near-miss" incidents (NMIs) in neurosurgery using an event reporting system, to inform the development of appropriate interventions. This retrospective study collected reports of NMIs in our hospital's neurosurgery operating room (OR) from January 2019 to January 2022 through an adverse event reporting system and anonymous surveys. We conducted intergroup difference analysis using t-tests and investigated factors contributing to NMIs using Pearson correlation coefficients. We further constructed multinomial logistic regression models to explore the important factors affecting the types of lost objects and search times. A total of 195 NMIs were included in this study, with the primary items lost being 62 brain cotton pads and 133 needles. Statistical analysis revealed that smaller pads (48.4%) and size 3.0 needles (49.6%) were the most commonly missed items, with the longest retrieval times. The likelihood of NMIs occurring was higher for nurses with junior and/or non-neurosurgical backgrounds (needles: 82.7%, pads: 83.9%). Furthermore, factors such as extended working hours, nighttime surgeries, larger incisions, and more surgical instruments all increased the incidence of NMIs. The results of the multinomial logistic regression model showed that the type and search time for lost needles in the OR were jointly influenced by multiple factors (p < 0.05) compared to cotton pads. The occurrence of NMIs is associated with various factors. Reporting NMIs and their causes helps identify solutions before adverse events occur, thereby enhancing patient safety.

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Cooperative intelligent transport systems (C-ITSs) are mass-produced and sold in Europe, promising enhanced safety and comfort. Direct vehicle communication, known as vehicle-to-everything (V2X) communication, is crucial in this context. Drivers receive warnings about potential hazards by exchanging vehicle status and environmental data with other communication-enabled vehicles. However, the impact of these warnings on drivers and their inclusion in accident reconstruction remains uncertain. Unlike sensor-based warnings, V2X warnings may not provide a visible reason for the alert, potentially affecting reaction times and behavior. In this work, a simulator study on V2X warnings was conducted with 32 participants to generate findings on reaction times and behavior for accident reconstruction in connection with these systems. Two scenarios from the Car-2-Car Communication Consortium were implemented: "Stationary Vehicle Warning-Broken-Down Vehicle" and "Dangerous Situation-Electronic Emergency Brake Lights". Volkswagen's warning concept was utilized, as they are the sole provider of cooperative vehicles in Europe. Results show that V2X warnings without visible reasons did not negatively impact reaction times or behavior, with average reaction times between 0.58 s (steering) and 0.69 s (braking). No significant distraction or search for warning reasons was observed. However, additional information in the warnings caused confusion and was seldom noticed by subjects. In this study, participants responded correctly and appropriately to the shown false-positive warnings. A wrong reaction triggering an accident is possible but unlikely. Overall, V2X warnings showed no negative impacts compared with sensor-based systems. This means that there are no differences in accident reconstruction regarding the source of the warning (sensors or communication). However, it is important that it is known that there was a warning, which is why the occurrence of V2X warnings should also be saved in the EDR in the future.

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This analysis examined systemic causes of Uncrewed Air Vehicle (UAV) accidents identifying operator, environmental, supervisory, and organisational factors through the use of the Human Factors Analysis and Classification System (HFACS). HFACS is a system-based analysis method for investigating the causal factors associated with accidents and incidents and has previously been used to reliably and systematically identify active and latent failures associated with both military and general aviation accidents. Whilst HFACS has previously been applied to UAV accidents, the last known application was conducted in 2014. Using reports retrieved from nine accident investigation organisations' databases, causal factors were coded against unsafe acts, preconditions, and failures at the supervisory, organisational, and environmental levels. Causal factors were assessed on 77 medium or large UAV mishaps/accidents that occurred over a 12-year period up to 2024. 42 mishap reports were deemed to involve a human factor as a causal factor. A large proportion of the mishaps contained factors attributed to Decision Errors at level 1 (Unsafe Acts) which was found to be associated with both the Technological Environment and Adverse Mental State at level 2 (Pre-conditions). Causal factors were identified at each of the other 3 levels (Supervisory, Organisational and External) with a number of emergent associations between causal factors. These data provide support for the identification and development of interventions aimed at improving the safety of organisations and advice of regulators for Uncrewed Air Systems.

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OBJECTIVES: Shared e-scooter service has been offered by various operators in Türkiye since 2019. The use of private and shared e-scooters is increasing, especially in large cities. This raises social concerns about the dangers e-scooters pose regarding traffic safety and injuries requiring medical attention. This study aims to investigate the accidents related to e-scooters in Türkiye to determine the contributing factors and accident characteristics. METHODS: In this study, accident reports (collision reports) for 780 e-scooter collisions that occurred in 2021 in Türkiye were examined, and 771 accidents were included. Accident data were obtained from the Traffic Department of the Ministry of Interior, General Directorate of Security. Descriptive statistics of the factors affecting e-scooter accidents are presented to determine the relationship and differences; chi-square tests, independent samples t-tests, one-way analysis of variance, and binary logistic regression methods were used. RESULTS: Male e-scooter riders are involved in crashes and injured approximately 4 times more often than female riders. The average age for men injured in e-scooter accidents is 30.4, and the mean age for women is 27.2. For both males and females, most injuries occurred in the 15 to 20 age group. Riders under the age of 18 constitute a significant proportion of the accidents (32.5%). Most e-scooter accidents occur on Mondays and during the month of August. Most accidents occurred between 12:00 p.m. and 1:59 p.m. (15.7%) and between 4:00 p.m. and 5:59 p.m. (15.7%), mainly during the daytime. About half of the accidents occurred at intersections. In 10.5% of accidents, the accident occurred at a pedestrian crossing. Approximately one-fifth of the accidents involved falls, and the most common type of collision was a side collision (44.2%). The binary logistic regression model showed that multivehicle accidents occur more often at intersections and during busy traffic hours. Single-vehicle accidents are more common on concrete roads and stone block roads. CONCLUSIONS: Deaths and injuries caused by road traffic accidents are a public health problem in Türkiye and constitute a significant health burden. If necessary precautions are not taken, this burden is likely to increase. We hope that the findings from this study will help reduce e-scooter accidents in Türkiye.

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As the elderly population grows, there is a greater concern for their safety on the roads. This is particularly important for elderly pedestrians who are more vulnerable to accidents. In Spain, one of the most aged countries in the world, the elderly accounted for 70% of all pedestrian deaths in 2019. In this study, the focus was on analysing the occurrence of elderly pedestrian-vehicle collisions in Spanish municipalities and how it is related to the built environment. The study used the hurdle negative binomial model to analyse the number of elderly and non-elderly pedestrian accidents per municipality in 2016-2019. The exploratory analysis showed that cities above 50,000 inhabitants were safer for the elderly, and larger provincial capitals had lower elderly pedestrian traffic accident rates. The occurrence of all pedestrian traffic accidents was linked to the socio-demographic features. For elderly pedestrians, land use was found to be influential, with a lower proportion of land covered by manufacturing and service activities linked to a smaller number of accidents. Results showed that improving road safety for older pedestrians may not necessarily compromise the situation for the rest of population. Hence, policymakers should focus on infrastructure improvements adapted to the needs of elderly pedestrians.

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Accidents between right-turning commercial vehicles and crossing vulnerable road users (VRUs) in urban environments often lead to serious or fatal injuries and therefore play a significant role in forensic accident analysis. To reduce the risk of accidents, blind spot assistance systems have been installed in commercial vehicles for several years, among other things, to detect VRUs and warn the driver in time. However, since such systems cannot reliably prevent all turning accidents, an investigation by experts must clarify how the accident occurred and to what extent the blind spot assistance system influenced the course of the accident. The occurrence of the acoustic warning message can be defined as an objective reaction prompt for the driver, so that the blind spot assistance system can significantly influence the avoidability assessment. In order to be able to integrate the system into forensic accident analysis, a precise knowledge of how the system works and its limitations is required. For this purpose, tests with different systems and accident constellations were conducted and evaluated. It was found that the type of sensor used for the assistance systems has a great influence on the system's performance. The lateral distance between the right side of the commercial vehicle and the VRU, as well as obstacles between them, along with the speed difference can have great influence on the reliability of the assistance system. Depending on the concrete time of the system's warning signal, the accident can be avoided or not by the driver when reacting to this signal.

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Objectives. This study conducted a comparative analysis of two catastrophic pipeline accidents in China in order to identify some common mistakes and lessons learned to prevent similar accidents. Methods. The 24Model was used in this study, which provides a universal pathway for accident analysis from the individual level to the organizational level. Results. There were similarities between the two cases in the aspects of the occurrence, development, emergency and causation at different levels: both were caused by leaks of pipelines and evolved into multiple explosions during emergency response; both leaks were caused by the corrosion of pipelines in the confined space of a damp or salt-spray environment; both were classified as 'responsibility accidents', and unsafe acts, such as the failure to identify hidden hazards of pipelines that were the direct cause of accidents, reflected the shortcomings of individual safety habitual behaviour in terms of knowledge, awareness, habits and psychology; weaknesses in the organizational management mainly concerned hazard identification, pipeline maintenance, emergency disposal, etc.; and there is not a good safety climate within the organization. Conclusions. Organizations should develop a closed-loop management system and strengthen the construction of safety culture, and the government should supervise the implementation of procedures.

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A stretch of road in the province of EL HAJEB, located in the central-south region of Morocco, is classified among the roadways experiencing an accumulation of fatal traffic accidents, with a particular involvement of freight transport vehicles. Investigation reports elaborated for these fatal accidents specify that these accidents occurred when drivers lost control of their vehicles due to brake system failures, resulting in multiple fatalities. However, these investigation reports did not provide root causes of this phenomena. Scientific research efforts in this field are directed toward preventive solutions and proposing a comprehensive analytical approach. This study aims to elucidate the mechanisms behind these specific accident phenomena on the identified stretch in the city of EL HAJEB. To achieve the study's objective and identify the triggering or contributing factors of these failures, we employed a novel approach combining the TRIZ and Ishikawa tools. This is a systematic methodology for analyzing potential causes of accidents, allowing us to clarify the intricacies of the specific phenomena leading to accidents while systematizing the analysis process, thus contributing to enhancing the effectiveness of investigative teams. This article contributes to introducing a new analytical tool in the field of accident analysis.

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Construction safety is of significance since construction accidents can result in loss of property and large numbers of casualties. This research aims to identify the critical causes of construction accidents by introducing a hybrid approach. The hybrid approach is developed to identify the critical causes of construction accidents by combining the human factors analysis and classification system (HFACS) model with complex network (CN) theory. A total of 863 construction accident cases were collected, and 46 causal factors were identified. Subsequently, the accident causal network was established, and six critical causal factors were extracted. The hybrid analysis approach is demonstrated with a real construction accident case, and the results demonstrate that the hybrid approach could better identify the critical causal factors. Consequently, this research enables the enhancement of understanding the HFACS framework and CN theory, as well as a contribution to safety management in the construction industry at different levels.

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Accident analysis methods are used to model the multifactorial cause of adverse incidents. Methods such as AcciMap, STAMP-CAST and recently AcciNet, are systemic approaches that support the identification of safety interventions across sociotechnical system levels. Despite their growing popularity, little is known about how reliable systems-based methods are when used to describe, model and classify contributory factors and relationships. Here, we conducted an intra-rater and inter-rater reliability assessment of AcciMap, STAMP-CAST and AcciNet using the Signal Detection Theory (SDT) paradigm. A total of 180 hours' worth of analyses across 360 comparisons were performed by 30 expert analysts. Findings revealed that all three methods produced a weak to moderate positive correlation coefficient, however the inter-rater reliability of STAMP-CAST was significantly higher compared to AcciMap and AcciNet. No statistically significant or practically meaningful differences were found between methods in the overall intra-rater reliability analyses. Implications and future research directions are discussed.

Practitioners who undertake accident analysis within their organisations should consider the use of STAMP-CAST due to the significantly higher inter-rater reliability findings obtained in this study compared to AcciMap and AcciNet, particularly if they tend to work alone and/or part of relatively small teams.

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Objectives This study aimed to provide greater insight into the characteristics of severe and extraordinarily severe gas explosion accidents (SESGEAs). Methods. The study analyzed the accident characteristics and causes of SESGEAs. As an example, we conducted a specialized case analysis using the 24Model (fourth edition) on the recent Baoma coal mine gas explosion. Results. SESGEA data are characterized by greater volatility, with significant differences in the geographical distribution, temporal distribution and attributed characteristics of the accidents. From the accident analysis: chaotic ventilation management was the most serious accident cause of SESGEAs; unsafe acts related to ventilation operations accounted for 18.51% of all unsafe acts; coal miners lack professional safety knowledge and have a serious fluke mentality in mining work; enterprises have insufficient enforcement of safety procedure documents, and lack of attention to the allocation of underground human resources and safety training systems; and the importance of safety, the role of the safety department and satisfaction with safety facilities have become the most serious missing items of safety culture. Conclusion. This study can provide important data support and management basis to assist mine operators in developing more targeted accident prevention strategies.

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Objective: Autonomous driving technology eliminates human errors, and thus it is a promising solution for reducing road traffic fatalities and injuries. While future autonomous driving technology may be able to reduce the number of collision accidents, it will not be able to avoid all collision accidents. This study is aimed to demonstrate why some accidents will still be unavoidable even with advanced perceiving and controlling capabilities.Methods: Because fully autonomous vehicles are currently in the laboratory stage, we used the prospective method to study the unavoidable accident of autonomous vehicles. Suitable traffic accident cases were screened from the China In-Depth Accident Study (CIDAS). Videos of the accidents were analyzed and the accidents were reconstructed using PC-Crash software. We assumed that target vehicle possesses near-perfect autonomous driving capabilities. Unavoidable accidents were determined based on vehicle dynamics and traffic constraints. The time from perceiving hazard to collision was calculated for each accident.Results: Among the 112 accidents screened, 15 cases of unavoidable accidents were identified. Three typical cases are presented in detail in this study. Based on the reasons why the target vehicles cannot avoid the collisions, we classified the unavoidable accidents into time-limit type and space-limit type. Time-limit means that vehicle cannot stop or steer out of danger in time, and space-limit means that the traffic environment does not have sufficient space for vehicle to avoid collision.Conclusions: Collision accidents will still occur even with perfect autonomous driving technology. We used the prospective method to investigate scenarios and characteristics of unavoidable accidents of autonomous vehicles. The time-limit type and the space-limit type were identified as two categories of unavoidable accidents. For the time-limit unavoidable accidents, the time from perceiving hazard to collision is typically not longer than 1.5s. The characteristics of unavoidable accidents and the estimated pre-crash warning time can provide some reference for establishing future occupant protection strategies. This study also showed the limitations of active safety and the necessity of passive safety.

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INTRODUCTION: The continuous growth in the use of e-bikes (in Germany mostly pedelecs that support pedaling up to 25 km/h) raises questions about the use of historic crash data for the development of road safety measures. The aim of this study was to address this issue, by conducting a longitudinal analysis of pedelec and bicycle crash data over a period of nine years to identify trends and to clarify whether such trends are specific to pedelecs. METHOD: We analyzed 95,338 police reported pedelec and bicycle injury crashes from 2013 through 2021. The dataset consisted of crashes from three federal states of Germany: Brandenburg, Hesse and Saxony. Data were analyzed with respect to sex and age distribution, time, location and type of accident, conflict partner, cause of crash and injury severity. RESULTS: Many of the analyzed variables showed a considerable degree of temporal stability, with differences as well as similarities between the two bicycle types staying quite consistent over the years. One notable difference was the mean age of the involved riders, with crashed pedelec riders being significantly older than conventional cyclists. At the same time, however, the mean age of these pedelec riders has decreased by eight years over time. Single vehicle crashes were consistently more common for pedelec riders than for cyclists. Similarly, pedelec rider crashes went with a higher injury severity over all the years. CONCLUSIONS AND PRACTICAL APPLICATIONS: While, on a more detailed level, we found differences between the two bicycle types, overall crash characteristics were remarkably similar and consistent over time. Our findings provide no clear argument for road safety measures that are specifically designed to target pedelecs. Instead, the stable crash total, and the increases in ridership of both bicycles and pedelecs, highlight the demand for new, innovative solutions to improve cycling safety in general.

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Partially automated driving functions (SAE Level 2) can control a vehicle's longitudinal and lateral movements. However, taking over the driving task involves automation risks that the driver must manage. In severe accidents, the driver's ability to avoid a collision must be assessed, considering their expected reaction behavior. The primary goal of this study is to generate essential data on driver reaction behavior in case of malfunctions in partially automated driving functions for use in legal affairs. A simulator study with two scenarios involving 32 subjects was conducted for this purpose. The first scenario investigated driver reactions to system limitations during cornering. The results show that none of the subjects could avoid leaving their lane and moving into the oncoming lane and, therefore, could not control the situation safely. Due to partial automation, we could also identify a new part of the reaction time, the hands-on time, which leads to increased steering reaction times of 1.18 to 1.74 s. The second scenario examined driver responses to phantom braking caused by AEBS. We found that 25 of the 32 subjects could not override the phantom braking by pressing the accelerator pedal, although 16 subjects were informed about the system analog to the actual vehicle manuals. Overall, the study suggests that the current legal perspective on vehicle control and the expected driver reaction behavior for accident avoidance should be reconsidered.

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A better understanding of factors associated with bicycle crashes can inform future efforts to limit crash risks. Many previous studies have analysed crash risk based on crash databases. However, these can only provide conditional information on crash risks. A few recent studies have included aggregate flow measures in their crash risk analyses. This study incorporates detailed bicycle flow to investigate factors related to bicycle crashes. Specifically, the study assesses the relative crash risk given various conditions by applying Palm distributions to control for exposure. The study specifically investigates the relationship between weather and time conditions and the relative risk of bicycle crashes at a disaggregate level. The study uses bicycle crash data from police reports of bicycle crashes from 2017-2020 in the greater Copenhagen area (N = 4877). The relations between the bicycle crash risk and the air temperature and wind speeds are found to be highly non-linear. The relative risk of bicycle crashes is elevated at low and high temperatures (0 °C ¿ x, x ¿ 21 °C). The results also show how decreasing visibility relates to increasing bicycle crash risk. Meanwhile, cycling during the early morning peak (7-8) and afternoon peak hours (15-18) is related to an increased risk of bicycle crashes. While some of the effects are likely spurious, they highlight specific conditions associated with higher relative risk. Finally, the results illustrate the increased risk at weekend night times when cyclists are likely to bike under the influence of alcohol. In conclusion, the analysis confirms that visibility, slippery surfaces, and intoxication are all factors associated with a higher risk of bicycle crashes. Hence, it is relevant to consider how infrastructure planning and preventive measures can modify the bicycle environment to minimise these risks.

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To achieve the sustainable development goals (SDGs) of power transmission and substation project construction, statistical analysis was used to provide an outline of safety accidents, the 4M1E method was applied for sorting out and analyzing the risk factors, and the Apriori algorithm was carried out for exploring the intrinsic interaction among risk factors based on association rule mining. The results showed that the safety accidents that happen in the construction of power transmission and substation projects were not much frequent, but deadly; the process of foundation construction and high fall was the most accident-prone process and injury type respectively. In addition, human behaviors were the foremost factors leading to accidents, and there was a strong correlation among the risk factors of low project management level, lack of safety awareness, and poor risk identification ability. For improving the security situation, measures should be taken for controlling human factors, performing flexible management, and strengthening safety training. In further research, more detailed and diversified accident reports and case data should be analyzed, and more consideration should be put on the weighted risk factor analysis to obtain more comprehensive and objective safety accident analysis results for power transmission and substation projects. This study highlights the risks in power transmission and substation project construction and introduces a novel method for better analyzing the intrinsic interaction among risk factors, which provides theoretical support for related departments to conduct sustainable safety management.

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PURPOSE: The spatiotemporal trend of the burden of injury due to occupational accidents in Iran, 2011-2018 were assessed at the national and subnational levels. METHODS: The burden of occupational injury was estimated using three datasets of occupational injury data, the employed population, and duration and disability weight of injuries. RESULTS: The disability-adjusted life years (DALYs), deaths, DALY rate, and death rate (per 100,000 workers) of occupational injury in Iran drastically decreased from 169,523, 2,280, 827, and 11 in 2011 to 86,235, 1,151, 362, and 5 in 2018, respectively. The DALY rates of occupational injury were significantly different by gender and age in a manner that the DALY rate of men was much higher than that of women and the DALY rates by age group in 2018 ranged from 98 for 50 y and over to 901 for 15-19 y. The shares of injury outcomes in the total DALYs in 2018 were as follows: 63.6% for fatal injuries, 17.4% for fracture, 7.9% for open wound, 7.3% for amputation, and 3.8% for other injuries. Over 83% of the DALYs was observed in three economic activity groups of construction, manufacturing, and community, social, and personal service activities. The three provinces with the highest DALY rates in 2018 were Markazi, West Azarbaijan, and East Azarbaijan, respectively. CONCLUSION: Despite the decreasing temporal trend, the burden of occupational injury in Iran in 2018 was high. The high-risk groups and hot spot provinces should be taken into more consideration for further reduction of the injury burden.

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OBJECTIVE: Implementing usage-based insurance (UBI) schemes captured the attention and has found a better practical foundation, thanks to the presence of probes for recording the driving and traveling data. The UBI is believed to provide an incentive measure through premium discounts for correcting driving and traveling behavior. However, the success of UBI implementation is dependent on different factors including the presence of alternative insurance plans, the level of privacy concerns among society, and the amount of trust within the society. Therefore, designing proper discount schemes which affect UBI acceptance by drivers and its profitability for government and insurance companies differs across countries and different scenarios. We aim to investigate the profitability of UBI Pay-As-You-Speed in Iran with a focus on the government and insurance companies. This study will be beneficial for policymakers seeking to understand the potential impact of UBI Pay-As-You-Speed in Iran. METHODS: The research is conducted on a synthesized population with acceptance and accident frequency models based on a self-reported survey. We assumed six UBI schemes based on previous research. The acceptance model is a logit discrete choice model and the accident frequency is based on Poisson regression. Costs of crashes are estimated based on one-year data of the Central Insurance company in Iran. After models' estimation, the simulated population is used to estimate the resultant profit of private insurance companies and government. RESULTS: It is found that the scheme with no premium discount and no rental prices for the required monitoring device, results in the highest revenue for the government. Moreover, with an increase in the probe penetration rate, the profitability of the government increases with a greater reduction in crashes. However, this trend is not observed for insurance companies because the cost of the monitoring device and premium discounts offset the profits gained from prevented crashes. CONCLUSION: The presence of the government as a main actor in facilitating the implementation of UBI schemes is essential or private insurance companies would be reluctant to provide these schemes to customers.

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Objectives: This review aimed to integrate previous research to gain a deeper understanding of which individual factors are associated with reduced accident involvement, and which factors may be linked to success during emergency situations when they do occur. Better understanding how the human will react in these situations, combined with technological enhancements is vital to risk mitigation and ensuring successful performance. This review will also identify gaps in the literature that have yet to be addressed. Method: A systematic literature review was undertaken, beginning with 18,319 articles from three multidisciplinary databases. After careful review and exclusion, a final sample of 22 relevant articles were retained. This analysis was spread across various high risk, sociotechnical industries, including aviation, rail, mining, nuclear power, etc. Findings: The findings show that previous research has identified cognitive ability, leadership, situation awareness, personality, and risk perception as the most prominently considered factors in reducing accident involvement. Training, skill, situation awareness, and emotional stability were the most commonly associated factors to success through emergencies. Conclusion: While the research around individual differences impacting success during emergencies is scarce, this review provides future direction on potential factors influencing reduced accident involvement and/or potential factors that could influence a person's success through a disaster or emergency. This information could be implemented in recruitment and training of front-line workers in high-reliability organisations to reduce risk, increase safety and work towards reducing the number of accidents.

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In the past decades, marine accidents brought the serious loss of life and property and environmental contamination. With the accumulation of marine accident data, especially accident investigation reports, compared with subjective reasoning based on expert experience, data-driven methods for analysis and accident prevention are more comprehensive and objective. This paper aims to develop a content-aware corpus-based model for the analysis of marine accidents to mine the accident semantic features. The general research framework is established to combine accident data, expert prior knowledge, and semi-automated natural language processing (NLP) technology. The NLP models are optimized, fused, and applied to the case study of ship collision accidents. The results show that the proposed model can accurately and quickly extract hazards, accident causes, and scenarios from the accident reports, and perform semantic analysis for the latent relationships between them to extend the accident causation theory. This study can provide a powerful and innovative analysis tool for marine accidents for maritime traffic safety management departments and relevant research institutions.

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