شرایط کار، علائم روانی/جسمی و حوادث ناشی از کار. مدل های شبکه های بیزی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|29183||2012||15 صفحه PDF||سفارش دهید||9430 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Safety Science, Volume 50, Issue 9, November 2012, Pages 1760–1774
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. Highlights ► This study analyzes the influence of working conditions on occupational accidents. ► Bayesian networks were use to analyze the Spanish Survey of Working Conditions. ► Relations between hygiene, ergonomics, job demands, and accidents were quantified.
Work conditions and organization are elements that contribute to work accidents. So work safety requires that safe working conditions should not create significant risk of people being rendered unfit to perform their work. Health was defined by the World Health Organization as “a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity” (WHO, 1998). This definition has formed part of the Constitution of the WHO since it was established in 1948. The constitution also recognizes that health is one of the fundamental rights of human beings, and that the attainment of the highest standard of wellbeing is dependent upon the co-operation of individuals and nations and upon the provision of health and social measures. Health and safety at work is therefore aimed at creating conditions, capabilities, and habits that enable the worker and his or her organization to carry out their work efficiently and in a way that avoids events which could cause them harm. Occupational accidents cause considerable physical, psychological, and social harm to victims. In addition to the physical and psychological harm, occupational accidents have detrimental effects on the worker’s social life and, in turn, can lead to a worsening of the finances of the worker, of his or her company, and of society as a whole. Preventing or reducing the number of accidents and minimizing their consequences are among the main objectives of health and safety in companies. Theories have been put forward attempting to account for occupational accidents and these have emphasized human error, the worker being distracted, and so on. Nevertheless, accidents happen because there is a risk, and that risk stems from a set of working conditions which influence the behavior of the worker. With appropriate control of those working conditions and, therefore, of the associated risk, it may be possible to prevent accidents, regardless of the individual factors of each case. Studies into health and safety at work, aimed at preventing injury, have traditionally concentrated on the physical aspects, dealing with ergonomic factors and musculoskeletal disorders (De Jong et al., 2003, Ghahramani, 2000, Ghosh et al., 2010 and Hess et al., 2004) or hygiene conditions such as temperature and noise (Anttonen et al., 2009, Ashraf et al., 2005 and Morabito et al., 2006). Actually, a growing amount of research has, however, been focusing on establishing links between the psychological factors and accident rate (Bardera Mora et al., 2002, Dwyer and Raftery, 1991, Gillen et al., 2002, Glasscock et al., 2006, Hilton and Whiteford, 2010 and Sobeih et al., 2009). These studies analyzed the influence of the stress in the health and safety of workers. Results indicated that stressors and stress symptoms were predictors of occupational accidents. Stress has now become a relevant and socially recognized phenomenon, frequently linked to work and labor activities. There is no globally accepted definition of stress and, due to its wide diffusion, the term is used to refer to a wide variety of states (Belloch et al., 1995, Kasl, 1978, Lazarus and Folkman, 1984, McGrath et al., 1970 and Selye and Ogilvie, 1956). The International Labour Organization (ILO, 2012) refers to occupational stress as the harmful physical and emotional response that occurs when the requirements of the job do not match the capabilities, resources, or needs of the worker (Gabriel and Liimatainen, 2000). Several studies have focused on the influence of work-related factors on stress, including ergonomic conditions (Hoyos, 1995), work shifts (Barton, 1994 and Cervinka, 1993), excessive workload (Barnett and Brennan, 1995 and Perrewe and Ganster, 1989), job demands and job control (Karasek et al., 1981 and Karasek et al., 1988), monotonous and repetitive tasks (Melamed et al., 1995), work flexibility (Russell et al., 2009), conflicts and ambiguous workload (Bedeian and Armenakis, 1981 and Cosway et al., 2000), and a lack of co-worker support (Brage et al., 1998, Karasek and Theorell, 1994, Mirowsky and Ross, 2003 and Thompson et al., 2005). Turning to the more physical aspects, another health issue which has been highlighted in research over the last few decades is the problem of musculoskeletal disorders or physical symptoms. These include disorders of the muscles, tendons, nerves, or joints which can occur in any part of the body, although they most commonly affect the neck, back and upper extremities. While they may be due to factors outside the workplace and may even have personal causes, working conditions are linked directly to musculoskeletal disorders. The prevention of musculoskeletal disorders is directly linked to the correct design of the job (for example, allocated space or provision of adequate lighting), and to the physical demands (for example, moving heavy loads and doing repetitive tasks). In addition to being a problem for the health of workers, these disorders constitute a significant financial burden on society. Most of the financial losses associated with occupational injuries and illnesses involve musculoskeletal disorders (Takala, 2002), and these disorders are the main cause of the loss time injuries in developed countries (Brage et al., 1998 and Woolf and Pfleger, 2003). Although it is undeniable that these physical symptoms stem from the job design, there is also literature (Feuerstein et al., 2004 and Van den Heuvel et al., 2005) which claims that these symptoms are caused or aggravated on many occasions by psychological factors. Concluding, emphasis is currently being placed on improving working conditions in order to reduce occupational accident rates, with global models of greater complexity, including physical and psychological symptoms as mediating factors. This is about understanding the causes with a view to establishing corrective measures and analyzing working conditions and the risks to which the worker is exposed. For example, Goldenhar et al. (2003) proposed a model showing the relationship between job stressors and injury/near-miss outcomes. The three-part model was comprised of job stressors as the predictor variables, psychological/physical symptoms as mediators, and injuries/near-misses as final outcomes or results. The main strength of the proposed model was that it took into account the possibility of all three components. Lopez-Araujo and Segovia (2010) studied a model which analyzed the relationship, through psychological and physical malaise, between the organizational variables of stress in the workplace, safety climate and social support with regard to accidents, and incidents involving employees working in the construction industry. Finally Abbe et al. (2011) developed a model to investigate the degree of relationship between job stressors, physical and psychological symptoms, occupational incidents and accidents, and days of work lost, basing on the model proposed by Goldenhar et al. (2003). The objective of this study was to create a model comprising the predictor variables (hygiene conditions, ergonomic conditions, and job demands) in order to determine which of them affect – and how much they affect – the mediating variables (psychological disorders and musculoskeletal disorders) and finally, occupational accidents. To this end, it has been proposed using Bayesian networks (BNs) models that provide information of the relationship between all the studied variables. The BN model is a data mining technique that permit extracting knowledge from a database; in this case, the models have been created from the results of the VI NSWC of Spain. The Bayesian network (BN) method is becoming increasingly popular. It has been used in several knowledge areas, such as medicine (Antal et al., 2004), ecology and natural resources management (Borsuk et al., 2004 and McCann et al., 2006), geology (Rivas et al., 2007), lifecycle engineering (Zhu and Deshmukh, 2003), software engineering (Fenton et al., 2007), and reliability (Langseth and Portinale, 2007). Bayesian networks are also being applied to other safety related research. For instance, the paper by Ren et al. (2008) aimed to contribute to offshore safety assessments by proposing a methodology to model causal relationships with a BN capable of providing graphical inter-relationships and of calculating numerical values for the likelihood of each failure event occurring. Zhou et al. (2008) proposed a BN model to establish a probabilistic relational network among causal factors, including safety climate factors and personal experience factors, which exert influences on human safety behavior. Martin et al. (2009) used BNs to analyze workplace accidents involving auxiliary equipment (ladders, scaffolding, etc.). This enabled them to identify the circumstances that have the greatest bearing on workplace accidents during these activities, such as the adoption of incorrect work postures, the duration of tasks, and a worker’s inadequate knowledge of safety regulations. Others have applied BNs to road accidents; Helai et al. (2008) developed a Bayesian hierarchical binomial logistic model to identify the significant factors affecting the severity level of driver injury and vehicle damage in road accidents at signalized intersections. Ozbay and Noyan (2006) represented incident clearance patterns with formalisms based on BNs to allow traffic operators to create case-specific incident management strategies in the presence of incomplete information and De Oña et al. (2011) showed the possibility of using BNs to classify road accidents according to the severity of the injuries sustained. Finally, McCabe et al. (2008) used BNs to demonstrate that great pressure at work, high levels of interpersonal conflict, and low-quality leadership were strongly associated with work-related health outcomes and accidents. Graphical models, us BNs models, provide a useful way of dealing with complex problems because of their capability to combine the robust probabilistic methods with the easy to understand of graphs. BNs exploit the Bayes’ theorem to create the conditional probabilities and, when necessary, to propagate uncertainties. BNs allow for the revision and updating of prior beliefs with the provision of new evidence (field data), while at the same time allowing for a relative view about the state of knowledge rather than an absolute view because the beliefs can be continuously updated with increasing knowledge or evidence. In the present study, the BNs models proposed have been very useful for predicting the probability of occupational accidents and symptoms under different working conditions or a mix of them. Also, BNs models have permitted the analysis of the relationships between the physical symptoms, psychological symptoms, and accidents.
نتیجه گیری انگلیسی
The complexity of occupational accident rates makes it necessary to carry out an in-depth analysis of working conditions in order to improve safety at work. Understanding the causal relationships between the environment and characteristics of the job and the accidents suffered in an organization enables us to implement a more efficient occupational health and safety management strategy. This study was based on multivariate probabilistic techniques applied to the set of data from the VI NSWC undertaken by the Spanish NISHW. The sample comprised 11,054 records of workers engaged in all business activities throughout Spain. Models of BNs were developed to establish relationships between certain working conditions grouped together under the headings of hygiene conditions, ergonomic conditions and job demands, classed as predictor variables, and occupational accidents, and the physical and psychological symptomatologies were established as mediating factors. The initial global results showed strong relationships between hygiene conditions and accidents, ergonomic conditions and physical symptoms, and work demands and psychological symptoms. The poor hygienic conditions increased the probability of an accident from 10.46% to 21.53%, the poor ergonomic conditions increased the physical symptoms from 32.32% to 47.66%, and the high job demands increased the psychological symptoms from 10.49% to 18.43%. The sensitivity analyses carried out also revealed a significant mutual influence between physical symptoms and psychological symptoms. Workers with three or more physical symptoms presented a probability of 19.64% of workers who suffered psychological symptoms, and vice versa, workers with three or more psychological symptoms had a probability of 60.50% of having physical symptoms. In turn, the increase in this symptomatology considerably increased the probability of occupational accidents. That is, the probability of accidents with physical and psychological symptoms rose to 19.03%. The study individually detailed the factors that had the greatest influence on the mediating variables and the final variable of the study. It should be pointed out that the items which had the greatest influence on physical symptoms were the whole set of factors associated with ergonomics, particularly those jobs which required the worker to adopt bad body positions and to stretch in order to reach items, to be subjected to vibrations caused by manual tools, machines, vehicles, etc., and to work at speed on repetitive tasks and on short timelines. Psychological distress was aggravated in those workers who faced high demands in their jobs, for example, when their workload was excessive, when they performed their labor activities in confined spaces, or were subjected to poor thermal conditions, working in high or low temperatures. Finally, occupational accidents had a strong direct relationship with the global set of hygiene and ergonomics factors studied, particularly the handling of harmful substances, the need to move heavy loads by using significant force, working on unstable or uneven surfaces, working in areas of difficult access, or stretching to reach items that are out of reach from the usual working body position. An optimization of these conditions relating to hygiene and ergonomics at work would bring the occupational accident rate down to four times below the initial value, according to the sensitivity analyses carried out. Although the networks did not show a strong direct influence of work demands associated with speed, the temporary nature and complexity of tasks on accident rates, they did show an influence on psychological distress among workers. And these psychological symptoms were clearly associated with an increase in the probability of occupational accidents. The results from this study indicated that poor working conditions have a significant effect on health and safety in companies. The factors analyzed which have the greatest influence on accident rates should therefore be taken into account in assessments of occupational hazards and emphasis should be placed on improving them with a view to minimizing occupational accident rates in companies. For future investigations, it would be recommendable to include other predictor variables relating to the organization of work (social support, control, training, leadership, involvement, etc.) and other working conditions associated with the field of industrial safety (the handling of machines, electrical hazards, etc.) in any assessment.