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Introduction: Modern approaches to Occupational Health and Safety have acknowledged the important contribution that continuous improvements to working conditions can make to the motivation of employees, their subsequent performance, and therefore to the competitiveness of the company. Despite this fact, organizational change initiatives represent a path less traveled by employees. Specialized literature has drawn on the fact that employees’ satisfaction presents both the foundation and catalyst for effective implementation of improvements to working conditions. Method: This paper conceptualizes the alignment of employees through measurement of job satisfaction and uses the Bayesian Network to assess the influence of human factors, particularly the cognitive, emotional, and behavioral aspects. Toward this aim, the Bayesian Network is evaluated through a cross-validation process, and a sensitivity analysis is then conducted for each influential dimension: emotional, cognitive, and behavioral. Results: The results reveal that these three dimensions are interrelated and have a direct influence on job satisfaction and employees’ alignment during the organization change. Further, they suggest that the best strategy for enhanced alignment and smooth conduct of organizational changes is simultaneous enhancement of the three dimensions. Practical applications: This study shows the influence of emotional, cognitive, and behavioral dimensions on job satisfaction and employees’ alignment during the organizational change. Furthermore, it elaborates the way to develop efficient and effective strategies for a successful change implementation and sustained alignment.

Introduction: This research relates the most important work-related factors affecting the development of hearing loss to the main methods used as medical assessment criteria in the diagnosis of occupational deafness. These criteria are the Speech Average Loss Index (SAL), the Early Loss Index (ELI) and the Percentage of Hearing Loss, and are applied to data obtained from audiograms performed on workers in occupational medical examinations. Method: Depending on the assessment method selected, these often return different results in grading an individual's hearing status and predicting how it will evolve. To address this problem, medical examinations (including audiograms) were carried out on a heterogeneous sample of 1,418 workers in Spain, from which demographic or personal data (gender, age, etc.), occupational data (noise level to which each individual is exposed, etc.) and other non-work-related factors (exposure to noise outside work, family history, etc.) were also gathered. Using Bayesian Networks, the conditional probability of an individual developing hearing loss was obtained taking into account all these factors and, specifically, noise level and length of service in the workplace. Sensitivity analyses were also carried out using the three scales (SAL, ELI and Percentage Hearing Loss Index), proving their suitability as tools the diagnosis and prediction of deafness. These networks were validated under the Receiver Operating Characteristic curve (ROC) criterion and in particular by the Area Under the Curve (AUC). Results: The results show that all three methods are deficient in so far as detecting preventive hearing problems related to noise in most workplaces. Conclusions: The most restrictive methods for detecting possible cases of deafness are the SAL index and the Percentage Loss Index. The ELI index is the least restrictive of the three methods, but it is not able to discriminate the causes of hearing problems in an individual caused by exposure to noise, either by its intensity level or by the time of exposure to noise. Practical Applications: The use of the three methods in the field of occupational risk prevention is extremely limited and it seems reasonable to think that there is a need for the construction of new scales to correct or improve the existing ones.

Multitasking while driving negatively affects driving performance and threatens people’s lives every day. Moreover, technology-based distractions are among the top driving distractions that are proven to divert the driver’s attention away from the road and compromise their safety. This study employs recent data on road traffic accidents that occurred in Spain and uses a machine-learning algorithm to analyze, in the first place, the influence of technology-based distracted driving on drivers’ infractions considering the gender and age of the drivers and the zone and the type of vehicle. It assesses, in the second place, the impact of drivers’ infractions on the severity of traffic accidents. Findings show that (i) technology-based distractions are likely to increase the probability of committing aberrant infractions and speed infractions, (ii) technology-based distracted young drivers are more likely to speed and commit aberrant infractions, (iii) distracted motorcycles and squad riders are found more likely to speed, (iv) the probability of committing infractions by distracted drivers increases on streets and highways, and, finally, (v) drivers’ infractions lead to serious injuries.

The use of communication technologies, e.g., mobile phones, has increased dramatically in recent years, and their use among drivers has become a great risk to traffic safety. The present study assessed the workload and road ordinary violations, utilizing driving data collected from 39 young participants who underwent a dual-task while driving a simulator, i.e., respond to a call, text on WhatsApp, and check Instagram. Findings confirmed that there are significant differences in the driving performance of young drivers in terms of vehicle control (i.e., lateral distance and hard shoulder line violations) between distracted and non-distracted drivers. Furthermore, the overall workload score of young drivers increases with the use of their mobile phones while driving. The obtained results contribute to a better understanding of the driving performance of distracted young drivers and thus they could be useful for further improvements to traffic safety strategies.

Today, the economic and social importance of occupational accidents is undeniable worldwide. Hence, research aimed at reducing this type of accident is considered a discipline of great interest for society in general. In this environment, working conditions play a fundamental role in the occurrence of accidents, and from their study, results can be obtained that provide information for decision-making that guarantee optimum conditions for the development of the employees’ tasks. Organizing the conditions of work execution is also a task that constitutes an essential aspect for a firm’s productivity, therefore, affecting their viability and results. In this work, a model is proposed for the study of different groups of working conditions and their influence on the probability of occupational accidents, in accordance with the data provided by the 7th National Survey of Working Conditions (VII NSWC). -e survey sampled 8892 workers active in all sectors of national production and is the last nation-wide survey administered in Spain. Bayesian networks (BNs) are used to generate a network that analyzes working conditions in all areas (27 variables have been included in addition to those corresponding to the sector and accident), and then, more specifically, the relationship that is established between ergonomic factors in the workplace, psychosocial factors of the worker, and the probability of an accident. -e results are achieved through the network obtained by highlighting some of the proposed variables. -e dependencies generated by the chosen variables are analyzed, and subsequently, the probability of accident for each of the productive sectors is determined. It is concluded that the ergonomic risks associated with physical strains in the workplace, together with the lack of job satisfaction on the employer’s behalf, both pose a very significant increase in the probability of being involved in an occupational accident, above the other variables of study.

The construction industry is one of the most important socio-economic sectors of the Spanish economy and one of the most affected by workplace accidents. An analysis of the data on accident rates is needed, in order to identify variables related with workplace accidents and to define the measures that need to be taken for their reduction. In this study, an analysis is conducted using Bayesian Networks and data from the 7th National Survey on Working Conditions (VII NSWC), to study the relations between workplace accidents, visiting a doctor for occupational reasons, time in the company/sector, information that workers have on workplace risks in the workplace, and information and training on workplace risks that workers have received over the past two years. The NSWC survey, which is conducted every four years, was administered to 8892 workers, in Spain, in 2011. The values derived from the analysis yield certain implications involving the aforementioned variables and how to reduce the probability of workplace accidents. From among the variables under study, information on workplace risks is the most important, with the probability of suffering an accident in the construction industry doubling when such information is insufficient. In accordance with the results, these implications could also help with decision-making focused on improvements to training and on-the-job information, intended both to prevent and to reduce workplace accidents.

Road traffic plays a vital role in countries&rsquo, economic growth and future development. However, traffic accidents are considered a major public health issue affecting humankind. Despite efforts by governments to improve traffic safety, the misalignment between the policy efforts and on-ground infringements, distractions and breaches reflect the regulatory failure. This paper uses the Bayesian network method to investigate unsafe behaviors and traffic accidents involving unlicensed drivers as a perspective for the regulatory alignment assessment. The findings suggest that: (1) unlicensed drivers are more likely to have unsafe driving behaviors, (2) the probability of being involved in a severe traffic accident increases when the drivers are unlicensed and decreases in the case of licensed drivers, (3) young drivers are noticeably more likely to engage in unsafe behaviors, usually leading to serious injuries and deaths, when their driving licenses are invalid, (4) women are more likely to engage in right-of-way violations and to have collisions with no serious injuries, contrary to unlicensed men drivers, who are involved in other types of traffic accidents resulting in serious injuries.

To reduce traffic accidents, an accurately estimated model is needed to capture the true relationships between the injury severity and risk factors. This study aims to propose a robust procedure to address the biases in police-reported accident data and subsequently to conduct sensitivity analyzes in order to estimate the variations in injury severity and distraction probability based on drivers’ behaviors/characteristics and psychophysical conditions. The results show that: (i) the excess speed will likely increase the probability of serious/fatal injury for drivers of all age groups by 10%; (ii) distraction and driver’ errors will likely increase the probability of serious/fatal injury in all drivers driving at a proper speed up to 1.5%; (iii) alcohol and drug consumption can significantly increase the probability of being distracted and making errors by 28.5% and 33.5% respectively; (iv) Alcohol consumption reduces the probability of driving at an appropriate speed in drivers under 25 by 40%. However, the results for drugs consumption are not as significant as the ones for alcohol consumption., This study was supported by funds from the following projects: “Modelo cuantitativo de Red Bayesiana con capacidad predictiva de la gravedad del accidente en función de los comportamientos y actuaciones de las personas” [Qualitative Bayesian Network Model for Predicting Accident Severity Based on Personal Behaviors and Actions]. Ref. SPIP2015-01852 supported by funds from DGT (Directorate-General for Traffic). “Modelización mediante técnicas de machine learning de la influencia de las distracciones del conductor en la seguridad vial” [Modeling the influence of driver distractions on road safety using machine learning techniques]. Ref. BU300P18 supported by funds from FEDER (Fondo Europeo de Desarrollo Regional, Junta de Castilla y León).

Hearing loss affects hundreds of millions of people all over the world, leading to several types of disabilities, ranging from purely physical to psychological and/or social aspects. A proper analysis to ascertain the main risk factors is essential in order to diagnose early and treat adequately. An exploratory analysis based on a heterogeneous sample of 1418 workers is presented in order to identify the main trigger factors for hearing loss. On the one hand, we recorded several medical and environmental parameters, and on the other, we created a model based on Bayesian networks in order to be able to infer the probability of hearing loss considering different scenarios. This paper focuses on three parameters: gender, age, and a family history of hearing problems. The results obtained allow us to infer or predict the best or worst auditory level for an individual under several different scenarios. The least relevant factor is the existence of a family history of deafness, followed by the gender factor, which slopes considerably toward better hearing for females, and most prominent of all, the age factor, given the large differences identified between the various age groups when the gender and family history of deafness variables remain constant.

[Background]: About 1.35 million people died in traffic accidents around the world in 2018, make this type of accidents the 8th cause of death in the world. Particularly, in Spain, there were 204,596 traffic accidents during 2016 and 2017, out of which 349,810 drivers were injured. The objective of this study was to understand to what extent seat belt non-use and human factors contribute to drivers injury severity., [Methodology The results are based on the information and 2016–17 data provided by the Spain national traffic department “Dirección General de Tráfico” (DGT). The discretization model and Bayesian Networks were developed based on important variables from the literature. These variables were classified as; human factor, demographic factor, conditioning factor and seat belt use., [Results]: The results showed that failure to wear the seat belt by drivers are likely to increase the risk of fatal and sever injury significantly. Moreover, distraction and road type road can contribute to the accident severity., This research is part of the project “Modelización mediante técnicas de machine learning de la influencia de las distracciones del conductor en la seguridad vial”, with the reference BU300P18 and supported by funds from FEDER (Fondo Europeo de Desarrollo Regional - Junta de Castilla y León).

Nowadays, Traffic Accidents (TA) have been considered a severe public health issue. In spite of the wide range of penalties to reduce the Unsafe Behaviors (UB), i.e. Unlicensed Driving (UD) and Distracted Driving (DD), many converging transportation studies related the degraded driving performance and increased TA and fatalities to the UB. When investigating the TA, these studies have examined effects of numerous parameters, however little attention has been paid to the driving license status of distracted drivers. This paper outlines a contribution to the existing knowledge and investigates the driving license status of the distracted drivers (technology-based distractions) involved in accidents using the Bayesian Network with a particular focus on the influence of the zone and the vehicle type. The dataset for this study has been prepared from a 3-year period database (2016-2018) of accidents in Spain provided by the Spanish Transportation General Department. Results showed that the zone and the vehicle type seem to increase significantly the probability of being unlicensed when engaging in distracted behaviors. Indeed, the probability of being unlicensed when driving motor vehicle and having DD increases from 6.11% to 9.46% on the road and to 42.93% on highways. However, in the case of motorcycles, the probability of being unlicensed when presenting DD increases from 6.11% to 16.89% on the street, 27.42% on the road and to 73.11% on the highway.

The aim of this study was to quantify the probability of committing a speed infraction by young drivers and to investigate to what extent listening music could affect young drivers’ emotions as well as their driving performances at the wheel. To achieve this aim, employing Bayesian networks, the study analysed different music styles, in which they resulted in sample drivers’ speed infractions. Gender and drivers’ experiences at the wheel were the other factors, which were taken into account when interpreting the study results. Variables taken into account in this study included type of music whilst driving, gender of drivers, and drivers’ driving experiences. These variables further incorporated into the study of other telemetric variables including acceleration, number of revolutions per minute (RPM) of the engine, brake, traffic, and other types of infractions other than speed, which were considered as dependent variables. A driving simulator was used, and different driving simulation studies were carried out with young people aged between 20 and 28 years. Each participant carried out three simulations by listening to different type of music in each journey. The study defined a conceptual model in which the data were analysed and evaluated mathematically through Bayesian networks. A sensitivity analysis was performed to evaluate the influence of music on driving speed. Based on the different variables, the study further analysed the probability of speed infractions committed by drivers and their adequate speed. The range of frequency probabilities varied between 96.32% (which corresponds to experienced male drivers who do not listen to music) and 79.38% (which corresponds to less-experienced female drivers who listen to music), which resulted in their happiness or aggression., FEDER (Fondo Europeo de Desarrollo Regional) for developing Castilla y Le´on´s region. *e title of the project is “Modelizaci´on mediante t´ecnicas de machine learning de la influencia de las distracciones del conductor en la seguridad vial-Modeling the influence of driver´s distractions on road safety through machine learning techniques.” Ref. BU300P18

Collaborative robots (cobots) are widely used in manufacturing industries however there are some concerns whether they distract workers and affect their performance. The aim of this study was to investigate whether there exist any differences between the workers' eye behaviors while interacting with a human or a Cobot to complete a task. A total of 5 participants were enrolled in this study and performed 10 similar tasks with the help of a Cobot and a human. In the present study the eye movements of participants were recorded and four performance measures including total fixation duration, number of fixations, pupil diameter and peak velocity of saccades were reported. The results indicate that the average fixation duration and the number of fixations in Human-Cobot interaction were slightly higher than that in Human-Human interaction. However, the difference was not statistically significant. Similarly, no noticeable between-group differences in terms of other measured variables were observed.

Traffic accident is the ninth leading cause of death in all age groups worldwide. The are many factors contributing to the road traffic accidents. However, human factors account for approximately half of the traffic accidents. More specifically, driver distraction is one of the major concerns for transport safety and it may be caused by using hand-held or hands-free mobile phone which impairs driver’s inattention for a period of time that and consequently may result an accident. Driving simulators are a useful tool, mainly aimed at educating and researching knowledge and skills in driving. The main advantage is that the simulation occurs in controlled environments to assess driving behavior without exposing the driver to a real risk. Therefore, the objective of the present study is to analyze the consequences of mobile phone use while driving. As driving simulator is an effective tool to evaluate drivers’ performance without exposing them to a real risk, the present study aims to conduct a driving simulator study to realize how the use of social media applications, individual characteristics would contribute to the likelihood of aggressive driving. Forty simulations were done and 43,259 data records were collected from people aged between 20-28 in this experiment. The Bayesian network method has been used to develop a global probabilistic model. The results indicate that the use of social media applications while driving will likely increase the likelihood of committing traffic infraction. In addition, the novice male drivers will highly likely to commit traffic infraction compared to other driver groups.

In this research we identify the main factors believed to trigger occupational hypoacusia in an effort to increase our knowledge of how this occupational disease occurs and develops. With this goal in mind, we have gathered various demographic/personal, occupational and non-occupational data from a heterogeneous sample of 1418 workers. The data selected include the noise levels to which the individuals in the sample are exposed. This entailed taking measurements at their respective jobs, as well as doing an objective assessment of their hearing ability, which required administering medical hearing tests. Lastly, the workers completed a survey on various habits and other factors deemed to be influential, and on the respondents’ own perception of their hearing. Bayesian networks were used to obtain the conditioned probability of developing hypoacusia based on the data collected from the sample. Specifically, for this study we used the general network created by the relationships between all of the factors associated with developing hypoacusia in order to analyze the influence individually and by grouping three specific variables: activity sector, noise level and time on the job. This work yielded a considerable database that can be used to conduct a multitude of analyses intended to study and predict the hearing acuity of the working population under different scenarios. Specifically, in the case at hand, the Bayesian network obtained indicates that the three factors analyzed influence the hearing of the individuals, though to different extents. The least influential factor involves the sector of activity, followed by the noise level on the job, which varies noticeably in favor of better hearing for workers in jobs whose noise levels are rated as low. Finally, we deemed time on the job (which is also related to age), as the most influential factor as it exhibits the largest differences among its potential states, with workers whose time on the job is rated as low or medium exhibiting the best likelihood of having good hearing.

Roundabouts are considered as one of the most efficient forms of intersection that substantially reduce the types of crashes that result in injury or loss of life. Nevertheless, they do not eliminate collision risks, especially when human error plays such a large role in traffic crashes. In this study, we used a driving simulator and an eye tracker to investigate drivers’ eye movements under cell phone-induced distraction. A total of 45 drivers participated in two experiments conducted under distracted and non-distracted conditions. The results indicated that, under distracting conditions, the drivers’ fixation duration decreased significantly on roundabouts, and pupil size increased significantly.

Road traffic injuries are now regarded as the eighth leading cause of death globally. For example, in 2016, 102,362 traffic injuries took place in Spain in which 174,679 drivers suffered injuries. These findings necessitated the development of the current study which focuses on the prime factors that cause this type of injuries. The aim of this study, therefore, is to explore the behavioral factors that entail a higher risk of suffering either a serious or a fatal injury for drivers. Methods. The findings are based on information and data provided by “Dirección General de Tráfico” (DGT) in Spain on traffic injuries that occurred in the year 2016. Reviewing a wide range of the literature, the authors identified the most influential variables and created a model using the Bayesian networks. The variables that define the model are grouped into four factors: vehicle factor, road factor, circumstantial factor and human factor. Results. The results suggest that the principal variables that determine a higher probability of serious or fatal injuries in traffic injuries are: lack of using appropriate safety accessories, high-speed violations, distractions as well as errors. Finally, the research shows the severity probability based on reason of displacement (“in itinere,” on business, or in leisure)., Ref. BU300P18” supported by funds from FEDER (Fondo Europeo de Desarrollo Regional - Junta de Castilla y León).

Human workers are envisioned to work alongside robots and other intelligent factory modules, and fulfill supervision tasks in future smart factories. Technological developments, during the last few years, in the field of smart factory automation have introduced the concept of cyber-physical systems, which further expanded to cyber-physical production systems. In this context, the role of collaborative robots is significant and depends largely on the advanced capabilities of collision detection, impedance control, and learning new tasks based on artificial intelligence. The system components, collaborative robots, and humans need to communicate for collective decision-making. This requires processing of shared information keeping in consideration the available knowledge, reasoning, and flexible systems that are resilient to the real-time dynamic changes on the industry floor as well as within the communication and computer network infrastructure. This article presents an ontology-based approach to solve industrial scenarios for safety applications in cyber-physical production systems. A case study of an industrial scenario is presented to validate the approach in which visual cues are used to detect and react to dynamic changes in real time. Multiple scenarios are tested for simultaneous detection and prioritization to enhance the learning surface of the intelligent production system with the goal to automate safety-based decisions.

Trabajo presentado en: 29th European Safety and Reliability Conference (ESREL), 22–26 September 2019, Hannover, The goal of this research is to explore the role of the collision partner – vehicle type and driver culpability – in incidents involving injuries to people cycling. Previous research has explored a range of factors affecting cyclist injury severity, but were more frequently focused on cyclist behaviour and/or road conditions. The database for our study includes a total of 12,318 drivers or riders of any vehicles involved in traffic injuries with victims in Spain in 2016, of which 7,488 are injured bicycle riders. The database used in our research was provided by Spain's National Traffic Department (Dirección General de Tráfico - DGT). This research uses Bayesian machine learning techniques. These have been recently used to study the severity of traffic injuries, since they provide a sound methodology for analyzing their causes and risks and predicting the probability of traffic injuries with serious injuries or fatalities. We have found proof that involvement of heavy vehicles substantially increases the likelihood of cyclists being killed or seriously injured, and that drivers are more likely than cyclists to be held responsible for the injury.

Road traffic accidents are currently between the seventh and tenth leading cause of death in the world, with approximately 1.35 million people killed per year. Despite extensive efforts by governments, according to the World Health Organization, road accidents still cause far too many deaths, especially among pedestrians, cyclists and two-wheel motor vehicle riders, who together account for almost 50% of road traffic fatalities. In particular, Spain had 410,974 traffic accidents between 2016 and 2019, involving 722,516 vehicles and 61,177 pedestrians with varying degrees of injury. This study uses the Bayesian network method to understand how the pedestrians’ responsibility and actions at the time of the traffic accident affect the injury suffered by said pedestrian, also considering the variables of the road infrastructure and vehicles at the accident site. The results confirm that the variables linked to the unsafe behavior of pedestrians, and their responsibility in traffic accidents, increase the risk of suffering serious or fatal injuries during an accident; for example, if a pedestrian is distracted this increases his/her probability of suffering a severe injury (27.86%) with respect to not being distracted (20.73%). Conditions related to traffic in high-speed areas, areas with no or poor lighting, and areas lacking sidewalks, also record increases in pedestrian injury, as is the case in the age group of pedestrians over 60 years of age.

Obtaining reliable and objective data on certain working conditions is necessary to analyse the causes and variables that can influence the development of hearing loss amongst the working population. Objective occupational data have been collected from a heterogeneous sample of 1418 workers in Spain, see “How activity type, time on the job and noise level on the job affect the hearing of the working population. Using Bayesian networks to predict the development of hipoacusia” (Barrero et al., 2018) [1]. Among the main factors analysed are the noise levels to which these workers are exposed, measured at their respective workstations, and the assessment of their hearing status, evaluated by audiometric medical tests. These factors provide information to predict the development of hypoacusia.

Healthcare professionals undergo high levels of occupational stress as a result of their working conditions. Thus, the aim of this study is to develop a model that focuses on healthcare professionals so as to analyze the influence that job demands, control, social support, and recognition have on the likelihood that a worker will experience stress.The data collected correspond to 2,211 healthcare workers from 35 countries, as reported in the sixth EuropeanWorking Condition Survey (EWCS). The results obtained fromthis study allowus to infer stress under severalworking condition scenarios and to identify themore relevant variables in order to reduce this stress in healthcare professionals, which is of paramount importance to managing the stress of workers in this sector. The Bayesian network proposed indicates that emotional demands have a greater influence on raising the likelihood of stress due to workload than do family demands. The results show that the support of colleagues, in general, has less effect on reducing stress than social support from superiors. Furthermore, the sensitivity analysis shows that, in high-demand and low-control situations, recognition clearly impacts stress, drastically reducing it.

This study analyses factors associated with cyclist injury severity, focusing on vehicle type, route environment, and interactions between them. Data analysed was collected by Spanish police during 2016 and includes records relating to 12,318 drivers and cyclist involving in collisions with at least one injured cyclist, of whom 7230 were injured cyclists. Bayesian methods were used to model relationships between cyclist injury severity and circumstances related to the crash, with the outcome variable being whether a cyclist was killed or seriously injured (KSI) rather than slightly injured. Factors in the model included those relating to the injured cyclist, the route environment, and involved motorists. Injury severity among cyclists was likely to be higher where an Heavy Goods Vehicle (HGV) was involved, and certain route conditions (bicycle infrastructure, 30 kph zones, and urban zones) were associated with lower injury severity. Interactions exist between the two: collisions involving large vehicles in lower-risk environments are less likely to lead to KSIs than collisions involving large vehicles in higher-risk environments. Finally, motorists involved in a collision were more likely than the injured cyclists to have committed an error or infraction. The study supports the creation of infrastructure that separates cyclists from motor tra c. Also, action needs to be taken to address motorist behaviour, given the imbalance between responsibility and risk., European Regional Development Fund (Ref. FEDER BU300P18).

La gestion de calidad en las organizaciones sociales, como es el caso de las ludotecas, se apoya en un conjunto de factores criticos como son el liderazgo, la planificacion de la calidad, la gestion de los recursos humanos, la gestion de los procesos, la participacion de los usuarios de los servicios y la mejora continua

Trabajo presentado en: 2nd International Conference on Higher Education Advances, HEAd’16. 2016. Valencia, In this paper we present a hands-on experiment for measuring learning through teamwork applied to solving a real problem. The experiment is part of the Production Systems course and involves designing a Poka-Yoke, but not theoretically, as is usually the case, rather an actual working mechanism. To this end, a practical problem is proposed for which a physical machine has to be designed to solve the problem. As part of the same exercise, a TPM is developed, also applied to a real case, such as assembling a bicycle. In the case of the Poka-Yoke, two simultaneous objectives are pursued: to avoid a defective product, and to maximize the production per unit time. The final score is assigned based on a measurement of these two parameters. Once the exercise is assigned, the teamwork is verified to be measured efficiently, even when the number of students is high. The physical design of the elements, as well as the simultaneous engagement by all the students in the exercise, served to considerably raise the motivation of the students.

Introduction Occupational stress is a common phenomenon in our society, and generates problems for both workers' health and the functioning of organizations. Over past decades numerous studies have examined occupational stress from the perspective of gender, offering somewhat contradictory results. Some of them found no differences and others indicated that either men or women suffer from greater amounts of occupational stress. Method The purpose of this study was to analyze gender differences in stress in situations that involve certain occupational demands. The data used were taken from a random sample population of 11,054 (5,917 men and 5,137 women) from the VI National Survey on Working Conditions (NSWC) which was conducted in Spain in 2007. To carry out this study, a probabilistic model was constructed using Bayesian networks, with the following variables related to task demands: working with tight deadlines, quick work, intellectually demanding work, complicated tasks, repetitive tasks, excessive work, and work demanding high attention levels. Results The results of this study reveal that: the indicators studied significantly increased stress levels; women initially had higher stress levels than men; and when exposed to determined task demands, stress differences between genders tended to increase. Impact on Industry Companies need to consider the gender of their workers when assigning tasks in high demand/stress jobs.

Each day thousands of workers suffer occupational accidents of varying degrees of severity. Accidents at work render workers incapable of carrying out their day to day activities, either temporarily or permanently, and they also have detrimental effects on family life, the company, and the general public. In order to reduce the occupational accident rate, it is necessary to determine the causes of those accidents. Although there are many different types of accidents, they generally stem from poor working conditions. The purpose of this study was to analyze the influence of working conditions on occupational accidents from data gathered in the VI National Survey of Working Conditions (VI NSWCs) in 2007. This survey utilized a random sample of the active population of Spain. The sample comprised 11,054 people (5917 males and 5137 females). In order to carry out the study, a probabilistic model was built using Bayesian networks. The model included the following variables: hygiene conditions, ergonomic conditions, job demands, physical symptoms, psychological symptoms, and occupational accidents. The study demonstrated that there were strong relationships between hygiene conditions and occupational accidents; it has been shown that poor hygienic conditions duplicate the probability of accident. Physical symptoms increased almost 50% due to poor ergonomic conditions. And finally, high job demands almost duplicated the psychological symptoms. The investigation also showed a high degree of interdependence between physical and psychological symptomatologies and the relationship between these and occupational accidents.

Many high-hazard industries around the world have explicitly recognized the critical role that human, management and organizational risk factors play in major accidents. The findings of accident investigations and risk assessments demonstrate a growing recognition that the cultural context of work practices may influence safety just as much as technology.The objective of this paper is to establish a relationship between the concepts of safety culture and organizational culture in a Nuclear Power Plant (NPP). This study permits the identification and quantification of the possible mechanisms for improving the safety culture in the NPP acting on organizational culture. It therefore provides a methodology to identify potential strategies for safety improvement.Probabilistic (Bayesian) Networks (BNs) have been used to determine the relationships between the organizational culture and safety culture in a quantitative form. To this aim, we considered data from a survey conducted of every employee at a Spanish NPP. The resulting data-driven models allow us to establish the probabilistic relationship among organizational culture factors, including the 12 OCI (Organizational Culture Inventory) scales, that have an influence on safety culture. The study yielded a ranking of organizational cultures that can be used to improve safety culture in a NPP. © 2012 Elsevier Ltd.

Occupational stress is a major health hazard and a serious challenge to the effective operation of any company and represents a major problem for both individuals and organizations. Previous researches have shown that high demands (e.g. workload, emotional) combined with low resources (e.g. support, control, rewards) are associated with adverse health (e.g. psychological, physical) and organizational impacts (e.g. reduced job satisfaction, sickness absence). The objective of the present work is to create a model to analyze how social support reduces the occupational stress caused by work demands. This study used existing Spanish national data on working conditions collected by the Spanish Ministry of Labour and Immigration in 2007, where 11,054 workers were interviewed by questionnaire. A probabilistic model was built using Bayesian networks to explain the relationships between work demands and occupational stress. The model also explains how social support contributes positively to reducing stress levels. The variables studied were intellectually demanding work, overwork, workday, stress, and social support. The results show the importance of social support and of receiving help from supervisors and co-workers in preventing occupational stress. The study provides a new methodology that explains and quantifies the effects of intellectually demanding work, overwork, and workday in occupational stress. Also, the study quantifies the importance of social support to reduce occupational stress. © 2013 Elsevier Ltd. All rights reserved., This study was supported by funds from the following project: “The Influence of Workplace Organization on Safety and Health”. Ministry of Labour and Immigration. Ref. 07/08.08 PH/mc.

Uno de los aspectos que ha condicionado la realizacion de este articulo, ha sido la influencia sobre el estres en la combinacion de las cargas familiares y laborales y de sus consecuencias directas en el mundo laboral y familiar. En este estudio, se han expuesto variables importantes que afectan al estres dentro de la familia como el cuidado de ninos, el cuidado de personas mayores y la realizacion de tareas domesticas del hogar. Por otro lado, y de manera directa con el mundo laboral, se ha podido comprobar que el ritmo de trabajo por si solo, afecta sobre el estres y mucho mas aun en combinacion con las cargas familiares como el cuidado de ninos, el cuidado de mayores y la realizacion de las tareas domesticas del hogar. Con respecto a las cargas laborales, el estudio realizado asegura que la variable ritmo de trabajo es la mas influyente sobre la probabilidad de sufrir altos niveles de estres tanto por si sola como en combinacion con las cargas familiares. Los resultados obtenidos demuestran que la combinacion de todas estas variables pueden generar altos niveles de estres que repercutiran tanto en el mundo laboral como el familiar. Realizar estudios de sensibilidad de las combinaciones entre las variables mas desfavorables, en este sentido, va a permitir controlar directamente los niveles de estres sufridos por las personas tanto en la familia como en el trabajo. De esta manera, se podra actuar sobre esas variables, tanto familiares (cuidado de personas mayores, cuidado de ninos y realizacion de tareas del hogar) como laborales (ritmo de trabajo) para conseguir reducir en lo posible la probabilidad de sufrir altos niveles de estres.

Uno de los aspectos que ha condicionado la realizacion de este articulo, ha sido la influencia sobre el estres en la combinacion de las cargas familiares y laborales y de sus consecuencias directas en el mundo laboral y familiar. En este estudio, se han expuesto variables importantes que afectan al estres dentro de la familia como el cuidado de ninos, el cuidado de personas mayores y la realizacion de tareas domesticas del hogar. Por otro lado, y de manera directa con el mundo laboral, se ha podido comprobar que el ritmo de trabajo por si solo, afecta sobre el estres y mucho mas aun en combinacion con las cargas familiares como el cuidado de ninos, el cuidado de mayores y la realizacion de las tareas domesticas del hogar. Con respecto a las cargas laborales, el estudio realizado asegura que la variable ritmo de trabajo es la mas influyente sobre la probabilidad de sufrir altos niveles de estres tanto por si sola como en combinacion con las cargas familiares. Los resultados obtenidos demuestran que la combinacion de todas estas variables pueden generar altos niveles de estres que repercutiran tanto en el mundo laboral como el familiar. Realizar estudios de sensibilidad de las combinaciones entre las variables mas desfavorables, en este sentido, va a permitir controlar directamente los niveles de estres sufridos por las personas tanto en la familia como en el trabajo. De esta manera, se podra actuar sobre esas variables, tanto familiares (cuidado de personas mayores, cuidado de ninos y realizacion de tareas del hogar) como laborales (ritmo de trabajo) para conseguir reducir en lo posible la probabilidad de sufrir altos niveles de estres.

The authors regret providing the incorrect affiliation of Dr. Susana Garcia-Herrero and Dr. M.A. Mariscal. The correct affiliations are given below: Susana Garcia-Herrero, Ph.D. Escuela Politecnica Superior, Universidad de Burgos, Spain M.A. Mariscal, Ph.D. Escuela Politecnica Superior, Universidad de Burgos, Spain J.M. Gutierrez, Ph.D. Instituto de Fisica de Cantabria (CSIC-UC), Spanish National Research Council, Spain

This editorial reviews the evolution of the concepts of safety and quality that have been used in the traditional workplace. The traditional programs of safety are explored showing strengths and weaknesses. The concept of quality management is also viewed. Safety management and quality management principles, stages, and measurement are highlighted. The concepts of quality and safety guarantee are assessed. Total Quality Management concepts are reviewed and applied to safety quality. Total safety management principles are discussed. Finally, an analysis of the relationship between quality and safety from data collected from a company in Spain is presented.