Ellipsoids evaluated by Zastawny et al. [20]. a and b are the major and minor semiaxis, respectively.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"511",leadTitle:null,fullTitle:"Different Views of Anxiety Disorders",title:"Different Views of Anxiety Disorders",subtitle:null,reviewType:"peer-reviewed",abstract:"Anxiety, whether an illness or emotion, is a term with historical roots even in the Bible, but it was not popular until the modern age. 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Examples of them include pneumatic conveying, fluidized bed reactors, cyclones, classifiers, or flow mixers. Industrial sectors where such processes are important are the chemical, food, or paper industries as well as electric energy production. Due to the complexity of the involved flow, a great majority of simulations are carried out under Reynolds-averaged Navier-Stokes (RANS) in connection with an appropriate turbulence model to describe the turbulent dynamics of the carrier phase.
Two main frames are employed for the description of complex multiphase flows: the two-fluid model or Euler-Euler and the discrete particle models or Euler-Lagrange. In both of them, particles are approximated as point masses being transported in the carrier phase flow field; the solution of the flow around individual elements is usually too expensive and cannot be afforded. In the two-fluid model, both phases are conceived as two interpenetrating continua [1] whose properties are described by sets of partial differential equations. In the Euler-Lagrange approach, the discrete elements are considered as individual objects whose dynamics is governed by a Lagrangian motion equation. Therefore, to obtain the discrete phase variables in the computational domain, a large enough number of discrete element trajectories must be computed. On each particle, appropriate forces act reflecting the various microprocesses taking place at the element scale such as fluid-particle turbulent interaction, particle-(rough) wall interactions, and interparticle collisions [2]. Such technique is especially appropriate for the description of disperse multiphase flow, where usually particles have a size distribution, in confined domains where particle-wall collisions play a predominant role as pneumatic conveying, separation, and classification processes. Both techniques, two-fluid model and Euler-Lagrange, have been applied mainly considering spherical particles. This means that the forces due to the flow (drag and lift) as well as the microprocesses modeling, wall-particle and inter-particle interactions, are assumed to be for spherical-shaped elements [3]. In practical situations, however, nonspherical particles are encountered, either of irregular shape, either with well-defined shapes (fibers or granulates). For example, the paper industry uses large amounts of turbulent liquid to handle and transport the fibers that compose the paper pulp. Besides, such particles in the flow experience particle Reynolds numbers larger than one, Re > 1. For such particles, the most relevant transport mechanisms such as aerodynamic transport, wall-particle interactions, and interparticle collisions are substantially different than those for spherical particles.
With the objective of performing the numerical simulation of turbulent flows laden with nonspherical particles, additional information about the forces and torques due to flow (drag and lift forces and pitching and rotational torques due to the shear flow and particle rotation) is needed. It is known that for regular nonspherical particles, that is, ellipsoids or fibers, such forces depend on particle orientation with respect to the flow. For instance, fiber orientation plays a major role in chemical processes as injection, compression molding, or extrusion in which the mechanical properties of the suspensions are determined by the orientation distribution.
For the Stokes regime, particle Reynolds number much lower than 1, the behavior of the nonspherical particles can be determined by analytical methods. Forces and torques acting on an ellipsoidal particle were analytically computed by Jeffery [4]. In a series of papers, Brenner determined the forces due to the flow acting on arbitrary-shaped nonspherical particles in the Stokes regime under different flow configuration by means of theoretical methods [5]. In the creeping flow regime, also with particle Reynolds number much lower than 1, Bläser [6] computed the forces acting of the surface on an ellipsoid in free motion for different flow situations, which allow him to suggest a simple criterion for particle breakup.
The drag coefficient for particle Reynolds number higher than 1 must be obtained by experiments, physical or numerical, as the analytical methods are not applicable any more.
The experimental studies to determine the drag coefficients for nonspherical particles employ wind tunnels or sedimentation vessels. For a moderately wide particle Reynolds number range, there exist results for thin discs [7], isometric irregular particles [8], cylinders and plates [9], discs [10], and discs and cylinders [11]. Drag coefficients were developed in all cases only for certain particle orientations. Compiling such results, different correlations have been developed in terms of particle shape [12, 13, 14]. As representative parameter of the particle shape, two options appear to be dominant: the spherical particle equivalent diameter
The first numerical computations of very small nonspherical particles in pseudoturbulent flow were performed by Fan and Ahmadi [22] and Olson [23]. The hydrodynamic forces and torques were computed by the theoretical coefficients of the Stokes regime. Olson [23] estimated the time step for the translation and rotation motions in function of the fiber length, obtaining the corresponding dispersion coefficients. Fan and Ahmadi [22] showed that the dispersion of both, translation and rotation, was reduced with the fiber length. However, Olson [23] found a different result in the case of ellipsoidal particles. Lin et al. [24] investigated numerically the distribution of the orientation of the fibers in a developing mixing layer, comparing the obtained results with experiments. The fiber length was smaller than the Kolmogorov scale, so they employed the forces due to the flow of the Stokes regime. Zhang et al. [25], Mortensen et al. [26], and Marchioli et al. [27] studied the transport and deposition of ellipsoidal particles in a turbulent channel flow using direct numerical simulation (DNS). Again the hydrodynamic forces and torques were computed with Stokes regime expressions. Beyond the Stokes regime, van Wachem et al. [28] and Ouchene et al. [29] studied a turbulent channel flow laden with ellipsoidal particles using LES and DNS, respectively, employing the flow coefficients developed by themselves in previous works.
Rosendahl group developed a model for the numerical computation of cylindrical and superellipsoidal particles in laminar and turbulent flows in the intermediate Reynolds numbers regime [30, 31, 32]. Particle angular velocity and orientation were computed by means of the Euler parameters. Using a linear relationship between the drag coefficient and the ellipsoid parameters, it was possible to establish a correlation valid up to Reynolds numbers of 1000. To estimate the influence of the orientation, a correlation between the maximum (90°) and minimum (0°) drag was employed. Drag force was calculated using the projected area perpendicularly to the flow. The lift force was expressed in function of the drag coefficient and particle orientation. Other lift forces, such as those due to the fluid velocity gradients or particle rotation, were not considered. The study case was a combustion chamber with straw particles, which were quite well approximated by cylinders. It was found that straw particles were better dispersed than spheres [30], a fact that properly illustrates the importance of the correct modeling of nonspherical particles motion. In a later work [32], other forces such as added mass and pressure force were also included. Drag coefficient was computed using the Ganser [15] correlation, making it possible to numerically compute the biomass combustion chamber.
This contribution aims to study the motion of nonspherical particles immersed in homogeneous isotropic turbulent (HIT) velocity fields built from kinematic simulation at moderate Reynolds numbers. Computations were performed in a tailored in-house code. Properties analyzed include the Lagrangian time autocorrelation function, the translational and rotational particle response, and preferential orientation of the nonspherical particles in the turbulent flow, all of them in terms of particle aspect ratio and inertia.
To build the trajectory of a regular nonspherical particle, it is necessary to solve for its translational as well as rotational motion. However, whereas translation is solved in an inertial frame, rotation is solved referred to the so-called particle frame. Thus, the relevant coordinate frames and the transformations between them have to be introduced.
Figure 1 illustrates, in the case of a cylindrical particle, the employed coordinate systems:
Illustration of a cylindrical particle and the employed coordinate systems.
Goldstein [33] gives the transformation between the comoving and particle coordinate systems, which is frequently employed in regular nonspherical particle tracking [31].
The time evolution of such Euler angles depends on the particle angular velocity regarding the particle frame axes. However, there is a difficulty in the sense that such time evolution equations present an unavoidable singularity. Therefore, instead of the Euler angles, the Euler parameters
And the transformation matrix
In the present study, the initial particle orientations are assigned by means of the Euler angles. From them, the corresponding Euler parameters are computed by Eq. (3), and with them, the initial transformation matrix is evaluated using Eq. (4). The Euler parameters evolve in time following Eq. (5), where the particle angular velocities are expressed in the particle frame of reference
The nonspherical particle motion equations in a general fluid flow [34] are written as:
Translational motion:
Rotational motion:
Here,
In addition to a sphere, the four ellipsoids of Zawstawny et al. [20] have been chosen. They have different sphericities and aspect ratio (see Table 1). In Table 1, a denotes the major semiaxis and b the minor semiaxis.
Shape | Aspect ratio | Sphericity |
---|---|---|
Ellipsoid 1 (prolate) | 0.88 | |
Ellipsoid 2 (prolate) | 0.99 | |
Disc (oblate) | 0.62 | |
Fiber (prolate) | 0.73 |
Ellipsoids evaluated by Zastawny et al. [20]. a and b are the major and minor semiaxis, respectively.
Using DNS for ellipsoidal particles immersed in a uniform flow, Zastawny et al. [20] determined correlations for the flow coefficients (drag
Here,
The developed correlations depend not only on particle Reynolds number
a values are listed in [20] as also coefficients b, c, r:
Moreover, for the cylinders, Vakil and Green [19] developed correlations for drag and lift coefficients depending on orientation, Reynolds number based on its diameter
The correlations are expressed as:
Coefficients
can be found in Vakil and Green [19].
However, expressions for the pitching and rotational torque coefficients are not provided in [19]. Therefore, for the cylinders, the approach of [31] has been assumed. In [31], the distance between the center of mass and the center or pressure in a cylinder,
Then, the pitching torque
Particle motion equations and correlations for cylinders and ellipsoids presented in this section have been implemented in an in-house code. The numerical integration of the ordinary differential equations that govern the motion of nonspherical particles has been performed by a fourth-order Runge-Kutta method, with small enough time steps to avoid numerical instabilities [35, 36]. The fluid velocity field in which particles are immersed has been built by the kinematic simulation technique described in the next section. It is known that Runge-Kutta methods do not satisfy the time-reversal property, a fact that makes such methods inappropriate for integrating energy-conserving systems, for instance. However, particle equations are dissipative systems (as they include viscous drag forces) and, for them, Runge-Kutta algorithms can be used [37] provided that the time step is small enough to keep the errors bounded.
There exist different options to calculate the Lagrangian properties in a turbulent flow. The starting point is the trajectory equation in which the position
Here,
The three-dimensional Eulerian velocity field to be employed in Eq. (18) is built as a series of random Fourier modes. The velocity field is solenoidal at each realization by construction. Moreover, the energy spectrum of the Fourier modes is prescribed, for example, by a power law, so the effects of small flow scales on Lagrangian statistics are directly included. Such KS velocity field is written as [39]:
Here,
Because of the construction of the velocity field given by Eq. (19),
To validate the spherical particles tracking in the KS velocity field, the values of particle Reynolds stresses (RS) in HIT have been selected. In this configuration, Hyland et al. [41] demonstrated that, as the fluid turbulence is homogeneous, particle RS only depend on time and they can be written as
Comparison of spherical particle Reynolds stresses, obtained with KS versus theoretical values for i=j.
Computations were performed in a tailored in-house code. The turbulent velocity field generated with KS resembles one of the fields worked in [39]. Such velocity field is characterized by a fluctuating velocity u’ = 1 m/s, a fluid Reynolds number of 104 resulting in a Kolmogorov length scale
The regular nonspherical particles studied have been the ellipsoids in [20] and the cylinders in [19]. In all cases, particles have the same particle volume equivalent diameter
If the Kolmogorov time scale
Simulations proceed in the following way: for each KS realization of HIT fluid velocity field, a particle is located in the center of the domain with zero initial velocity; particle translational and rotational motion is computed from Eqs. (6) and (7), its orientation is calculated from Eq. (1), and its trajectory is built; particle tracking lasts for around 10 fluid integral time scales; and particle properties are stored every second for evaluation. Such process is carried out a sufficient number of times to reach significant statistical results. In this study, statistics has been performed based on 105 KS realizations.
In the following section, the results of the particle Lagrangian time autocorrelation function, the translational and rotational particle response, and preferential orientation of the nonspherical particles in the turbulent flow are analyzed as function of their shape and effective Stokes number.
The Lagrangian autocorrelation function
Computed RL,tτ curves for ellipsoidal particles [20] (left) and cylindrical particles [19] (right). Fluid Lagrangian and Eulerian curves are included for comparison.
As it can be readily seen from Figure 3, higher inertia particles are characterized by larger integral Lagrangian time scales (ILTSs) (defined as the integral up to infinity of
Moreover, for intermediate inertia, the curves for all particle shapes nearly collapse in a single curve. On the other hand, a shape effect is noticeable for the heaviest particles, where the various shapes present differences in their curves. It is interesting to realize that ILTSs of higher aspect ratio (AR) are below those of smaller AR, for both ellipsoids and cylinders. This effect is a Reynolds number effect due to the dependence of drag coefficient on shape and AR: an interaction between translation and rotation motions occurs that results in a spreading of the particle effective Stokes number. As a consequence, particles with higher Reynolds numbers also have larger effective inertia (reflected on an increased Stokes number) and, therefore, their LAF decreases slower, implying a higher ILTS. In the ellipsoids case, it happens that those of typ. 2 present a
In an analogous way to translational LAF, a rotational autocorrelation function (RAF)
where the particle angular velocity is denoted by
The obtained results for the Lagrangian rotational autocorrelation function of the ellipsoids of Zastawny et al. [20] are shown in the left part of Figure 4, whereas those of the cylinders of Vakil and Green [19] are in the right side of such figure. Again, horizontal axis is the nondimensional time delay,
Computed RL,rτ curves for ellipsoidal particles [20] (left) and cylindrical particles [19] (right).
In the case of cylinders (Figure 4, right), the RAF curves for all AR and inertias are different. For the smallest inertia particles, RAF decreases with increasing AR, similar to what was found for LAFs in the translational motion. Moreover, the
Next, the response of the nonspherical particles to the fluid fluctuating velocities is analyzed for both translation and rotation motions. Figure 5(a) shows the behavior of the particle’s relative linear root mean square (rms) velocity, that is,
Relative particle rms of particle linear velocity (a) and angular rms velocity (b). In all cases, the dependence on aspect ratio and inertia is considered. Closed symbols refer to prolate ellipsoids and open symbols to cylinders.
As inertia increases,
Figure 5(b) presents the behavior of the particle angular rms velocity,
In the following, the correlation relative velocity direction-particle orientation is analyzed depending on inertia and aspect ratio. A well-known fact is that regular nonspherical particles falling through a still liquid at intermediate Reynolds numbers tend to be oriented in a determined direction. Cylinders and prolate ellipsoids are prone to keep their symmetry axis (z’ in Figure 1) perpendicular to the flow, thus maximizing drag. Differently, discs and oblate ellipsoids tend to move with the symmetry axis aligned with the flow, also maximizing drag [43]. However, spheroidal Stokes particles only show a preferential orientation if a persistent velocity gradient exists [27]. Therefore, in HIT flow where there are no mean velocity gradients, a Stokes particle will not have any preferred orientation.
Newsom and Bruce [44] analyzed the influence of turbulence on preferential alignment of quite elongated fibers with
Previous reasoning is valid too for another kind of nonspherical shapes as disc-like, cylindrical, or ellipsoidal [45]. For high Reynolds numbers, that is, Re > 100 appears a secondary motion overimposed to the particles predominant movement direction. Such secondary motion is promoted by a wake instability and vortex detachment from the rear surface of the particles. Two main kinds of secondary motion can be observed: large quasi-periodic swings along the main path, and a more or less chaotic tumbling forming a definite angle with the main motion direction. There is a coupling between this kind of oscillatory motion and the wake instability [10]: a vortex detachment follows at the end of a particle swing. Nevertheless, in the present study, such secondary motions do not appear as the considered particle Reynolds number is not large enough, that is, Re < 40.
Let now
Orientations probability density functions (Pdfs) of prolate ellipsoids (a) and cylinders (b) regarding the relative flow direction.
Orientation probability density functions (Pdfs) of disc-like particles regarding the relative flow direction.
As it is observed in Figure 6(a), it is found that prolate ellipsoids do manifest preferential orientation with respect to the relative velocity. Of course, spherical particles do not have a preferred orientation and the corresponding Pdf is a horizontal line (black color). Prolate ellipsoids have a preference for orientating its symmetry axis orthogonal to the relative flow, tending to maximize the drag, similar to what occurs in particle sedimentation studies. The orientation preference increases with inertia, which is quite similar for all aspect ratios considered in this study.
On the other hand, as it is presented in Figure 6(b), cylinders seem not to have any preferred orientation in the HIT KS velocity field, being all the curves pretty flat. Only for the case of higher AR and lowest inertia, the curves show a trend to be slightly higher for values of
For the discs, Figure 7 shows that there is a clear trend of the particle symmetry axis to be aligned with the relative flow, again maximizing drag, similar to the results obtained for sedimenting particles in stagnant fluid. Such trend is more marked when particle inertia increases.
In this study, regular nonspherical particle responsiveness to HIT flows has been investigated in combination with KS of fluid velocity field. The main results obtained are the following: the particle LAF reduces when particle AR is augmented, because effective particle inertia decreases if aspect ratio increases; this is true for both translational and rotational time autocorrelation functions. In the case of cylinders,
The financial support of Universidad Autónoma de Occidente is gratefully acknowledged.
A work accident is defined as an accident that a worker suffers as a result of an incident that occurred suddenly due to an external reason for the work the worker performed for his employer while he was under the authority of the employer [1]. Before determining the causes of work accidents, the conditions that are accepted as being in the category of work accidents should be investigated. According to Law on Social Insurances and General Health Insurance No. 5510, the events that are accepted as work accidents are as follows: all accidents that include the period when the insured worker is in the workplace, the employee could not do his own job when he is sent to another place by the employer due to a job handled by the employer at the time, when female workers with small children are on maternity leave, and when the workers are collectively brought to another place by a vehicle provided by the employer [1].
Today, work accidents have become one of the most critical issues for our economy. The economic and psychological costs of these accidents have become a pressure factor in business life. Even if there is no accident, the constant risk to workers because of working conditions has become a major psychological problem [2].
The occurrence of accidents at workplaces and work-related diseases is of utmost importance in many respects such as protecting employee rights, fulfilling social responsibilities, and creating legal regulations. Although interest in work-related diseases began in the time of ancient Greece, work accidents and work-related diseases became an important issue during the Industrial Revolution and caused various labor movements and unionized employees to demand their rights. From the 19th century onwards, insurance companies began to offer insurance that covered work accidents and work-related diseases. In terms of Turkey, even though certain regulations had been in place since the 19th century in the Ottoman period, they were rather limited. After the establishment of the Republic of Turkey, laws were issued in limited areas, and then their scope was extended, covering all workers. To date, these laws have been updated many times, and improvements have been made [3].
Although technological developments have increased occupational health and safety, the alienation towards the job brought about by specialization, and a decreased sense of investment in the job has caused loss of attention during work which has led to work accidents. In this respect, creating a safe environment is of great importance in the working environment, but creating this environment is a rather difficult process because, apart from the current organization and working group, a safe climate appeals to individual perceptions [4]. In this context, it is also important to reduce stress in the working environment. There are studies indicating that work accidents increase as the level of stress increases in the work environment. In situations where stress, fear, and disgust are present, individuals experience more cognitive dysfunction and experience more accidents [5]. This damages the safety climate in the workplace. When a work accident occurs, workers cannot work efficiently because they are afraid for their own safety. For this reason, it is particularly important to provide an Occupational Health and Safety System in every workplace as well as to conduct studies on this issue [6].
Worldwide, 2.78 million workers die every year due to work accidents and work-related diseases. About 86.3% of these deaths are caused by work related diseases, and 13.7% are caused by work accidents. In non-fatal accidents, the number of injuries is higher. While for young workers it was determined that the accident rate was much higher, work-related diseases were lower. This situation stems from the situation of cumulative exposure to factors that cause work-related diseases and their delay time [7]. When work accidents were analyzed from a historical point of view, it was seen that they have regularly increased every year until 2010. There was a sharp decline in 2012, but later on an increase was observed. From 2012 to 2016 work accidents increased by 382%, and 84.29% of the people who experienced a work accident in 2016, were men [8].
When looking at the situation regarding work accidents in Turkey in 2016, it was seen that 32,52% of accidents and 60.85% of fatal accidents occurred in businesses that employed fewer than 50 people. More than half of the accidents occurred in working hours during the day, and 28.23% occurred before noon. The three industries that experienced the most work-related accidents in 2016 in Turkey were identified as factory-made metal product manufacturing except machinery and equipment, building construction, and specialized construction activities. These three industries made up 19.46% of all work accidents. In addition, the three industries where work accidents leading to fatalities occurred the most were building construction, land and pipeline transportation, and construction of outdoor buildings. The share of fatal work accidents in these three industries in the total volume of accidents was 39% [9].
The percentage of fatal accidents by economic activity per 100,000 workers in Turkey was divided into various business categories. Accordingly, while the highest accident rate occurred in agriculture, construction and sewage, waste management, and reclamation activities followed agriculture, respectively [8]. Worldwide, the construction industry has one of the highest occurrences of accidents. The use of large and heavy machinery and equipment increases the severity of injuries and the risk of death [10]. Although improvements have been made in developed countries, the construction sector in developing and under-developed countries is still one of the industries with the highest frequency of accidents [11].
According to the UCTEA (Union of Chambers of Turkish Engineers and Architects) report, in 2016, 20 people died on the first day of work, and 78 people died during the first week of work. While the number of people who died in the first eight to 30 days of the job, was up to 165 people, as experience increased, the number of people who had a fatal work accident decreased. 42.61% of workers involved in work accidents have 1 month to 1 year of experience at their job [9]. This situation shows how important experience and work proficiency are in work accidents [12]. When looking at the gender distribution in work accidents in general, it was determined that women had fewer accidents than men. However, in industries where women make up a higher percentage of employees, the manufacturing of food products, buildings and landscaping activities, and food and beverage service activities are the top three industries in which women experienced the most accidents in 2016 [13].
In a globalizing world, companies must improve their safety performance in order to compete on a global scale. Work accidents leading to permanent injuries, the accidents resulting in workers being unable to work for a long time, or the accident resulting in death create huge costs for companies. Investing in equipment to prevent work accidents and educating individuals in this area are important in terms of reducing costs. The development of a safety culture throughout the workplace is important in this context [14].
Many work accident and safety-related regulations have been made in Turkey in order to improve safety, and significant progress has been made. Labor Law No.1475 has been in force in Turkey since 1971, and it was amended in 2003 with Labor Law No. 4857. Since 2012, Occupational Health and Safety Law No. 6331 has been in force. Along with these laws, various improvements have been made in occupational health and safety. With the regulation issued in 2012, public and agricultural workers, as well as all workplaces and all employees regardless of the number of employees and the type of work, are covered within the scope of the law. In addition, risk assessment and increasing the number and capacity of occupational health and safety laboratories became compulsory for all businesses [8]. The National Occupational Health and Safety Council Regulation was issued in 2013. The aim of the council is to improve conditions related to safety in work life and to create a safety culture [15].
In spite of the arrangements made and the increase in precautions taken in Turkey in recent years, they are still far below the standards of developed countries. For this reason, many non-governmental organizations have been established, and these organizations have focused on issues such as increasing the legal rights of employees, improving working conditions, and workers being protected from harassment during the process after work accidents [16]. Although organizational safety is the responsibility of the employer, employees also have great responsibilities; they need to be careful and act consciously to prevent work accidents [14]. In this context, it is of great importance that employees use the protective gear prepared for them correctly, that they perform the emergency procedures that must be carried out in the event of an accident completely, and inform the proper authorities immediately– thus avoiding loss of lives [17].
Work accidents and diseases affect the whole country economically, socially and, psychologically. Work accidents constitute many cost elements such as lost working days, decrease in production, recruitment and training of new workers, compensation payments, and health expenditures. This situation causes state and company policies to be disrupted and sometimes not realized. In addition, the loss of human capital and the high budget share of social aid provided to the victims hinder new investments. From the perspective of the worker, the individual’s loss of welfare, the psychological pressure, and loss of status that he and his family experience cause workers to feel as if they are a burden. In addition, accidents in the workplace also negatively affect other employees [6]. The aim of this study was to determine the socio-demographic and economic factors that are critical in individuals experiencing work accidents that result in injuries in Turkey. For this purpose, 10 factors were selected, and the impact of these factors on the probability of experiencing work accidents was examined. In this study, the Turkey Health Survey data made by the Turkish Statistical Institute (TSI) were employed.
The micro data set obtained from the Turkey Health Survey carried out by the TSI in 2008, 2010, 2012, 2014, and 2016 were used in this study. The Turkey Health Survey, which was first carried out in 2008, had been conducted every two years. The Turkey Health Survey was last conducted in 2016. With this survey, the aim is to minimize the information gap in the current structure by obtaining information for the health indicators that have a large share in the development indicators that show a country’s level of development. In addition to being a survey that reflects the whole country, it is also important in enabling both international comparisons and shedding light on national needs. The scope of this survey is households that are located in all settlements within the borders of Turkey. Institutions including soldiers and permanent residents of dormitories, prison, nursing homes, hospitals, etc. are out of this survey’s scope as well as locations (i.e. small villages, settlements of nomads, etc.) that are thought to be inadequate in terms of sample size (the number of population less than 20) have been excluded.
This survey was designed to give a total estimate for Turkey. A stratified two-stage cluster sampling method was used to obtain the data. The first stage sampling unit was randomly selected blocks from the clusters (blocks) containing an average of 100 household addresses with a proportionate stratification, and the second stage sampling unit was the household addresses systematically and randomly selected from each selected cluster [18, 19, 20, 21, 22].
In this study, the data from a total of 35,019 employees over 15 years old were employed, including 5473 people who participated in the Turkey Health Survey in 2008, 5238 people who participated in this survey in 2010, 10,436 people who participated in 2012, 7415 people who participated in 2014, and 6457 people who participated in 2016.
The dependent variable of this study was a work accident of an individual measured by the question, “Have you had an accident that caused injury in the past 12 months?” The dependent variable was a binary variable. In the established binary logistic regression model, the dependent variable was categorized as 1 if the individual had had a work accident and 0 if not.
The independent variables are survey year (2008, 2010, 2012, 2014, 2016), gender (male, female), age group (15–24, 25–34, 35–44, 45–54, 55–64, 65+), education level (did not finish school/illiterate, primary school, secondary school, high school, university), marital status (single, married), work schedule (part-time, full time), profession (managers, professional occupational groups, technicians/assistant professional occupational groups, staff working in offices, service/sales staff, qualified agricultural/forestry/aquaculture workers, craftsmen/craft-related jobs, plant-machine operators/installers and those who work in jobs that do not require qualification), general health (very good/good, moderate, bad/very bad), psycho-social support/being depressed (no, yes), and alcohol use (no, yes). Ordinal and nominal variables were defined as dummy variables in order to observe the effects of the categories of all variables to be included in binary logistic regression model [23].
Survey statistics in Stata 15 (Stata Corporation) were used to account for the complex sampling design and weights. Weighted analysis was performed. First, frequency analyses of the variables in the model were performed. Then, chi-square independence tests were performed in order to detect the relationship between whether individuals had experienced a work accident and socio-economic and demographical factors. Last, factors which influenced the work accident experience of individuals were determined with binary logistic regression analysis.
Socio-demographic and economic factors that are critical in work-related accidents resulting in injury in Turkey are presented in Table 1.
Variables | Work Accident Experience | n (%) | P | ||
---|---|---|---|---|---|
No | Yes | ||||
Survey year | 2008 | 5271 (15.5) | 202 (19.2) | 5473 (15.6) | 0.033b |
2010 | 5085 (15.0) | 153 (14.5) | 5238 (15.0) | ||
2012 | 10,131 (29.8) | 305 (28.9) | 10,436 (29.8) | ||
2014 | 7204 (21.2) | 211 (20.0) | 7415 (21.2) | ||
2016 | 6274 (18.5) | 183 (17.4) | 6457 (18.4) | ||
Gender | Female | 9885 (29.1) | 159 (15.1) | 10,044 (28.7) | 0.000a |
Male | 24,080 (70.9) | 895 (84.9) | 24,975 (71.3) | ||
Age | 15–24 | 3757 (11.1) | 164 (15.6) | 3921 (11.2) | 0.000a |
25-34 | 9478 (27.9) | 319 (30.3) | 9797 (28.0) | ||
35–44 | 9978 (29.4) | 308 (29.2) | 10,286 (29.4) | ||
45–54 | 6913 (20.4) | 197 (18.7) | 7110 (20.3) | ||
55–64 | 2790 (8.2) | 47 (4.5) | 2837 (8.1) | ||
65+ | 1049 (3.1) | 19 (1.8) | 1068 (3.0) | ||
Level of education | Did not finish school/illiterate | 2183 (6.4) | 59 (5.6) | 2242 (6.4) | 0.000a |
Primary school | 11,985 (35.3) | 470 (44.6) | 12,455 (35.6) | ||
Secondary school | 5104 (15.0) | 226 (21.4) | 5330 (15.2) | ||
High school | 7093 (20.9) | 214 (20.3) | 7307 (20.9) | ||
University | 7600 (22.4) | 85 (8.1) | 7685 (21.9) | ||
Marital status | Single | 8040 (23.7) | 266 (25.2) | 8306 (23.7) | 0.239 |
Married | 25,925 (76.3) | 788 (74.8) | 26,713 (76.3) | ||
Work schedule | Part-time | 2078 (6.1) | 55 (5.2) | 2133 (6.1) | 0.229 |
Full time | 31,887 (93.9) | 999 (94.8) | 32,886 (93.9) | ||
Occupation | Manager | 2459 (7.2) | 34 (3.2) | 2493 (7.1) | 0.000a |
Professional occupation group | 4815 (14.2) | 54 (5.1) | 4869 (13.9) | ||
Technician | 2502 (7.4) | 53 (5.0) | 2555 (7.3) | ||
Office worker | 1980 (5.8) | 22 (2.1) | 2002 (5.7) | ||
Service employee and sale representative | 5456 (16.1) | 134 (12.7) | 5590 (16.0) | ||
Qualified agricultural worker | 5164 (15.2) | 158 (15.0) | 5322 (15.2) | ||
Artist | 4659 (13.7) | 283 (26.9) | 4942 (14.1) | ||
Equipment and machinery operator | 2807 (8.3) | 125 (11.9) | 2932 (8.4) | ||
Non-qualified job worker | 4123 (12.1) | 191 (18.1) | 4314 (12.3) | ||
General Health | Very Good | 25,153 (74.1) | 706 (67.0) | 25,859 (73.9) | 0.000a |
Medium | 7276 (21.4) | 277 (26.3) | 7553 (21.6) | ||
Very Poor | 1529 (4.5) | 71 (6.7) | 1600 (4.6) | ||
Psycho-social support/ depression | No | 32,078 (94.4) | 969 (91.9) | 33,047 (94.4) | 0.001a |
Yes | 1887 (5.6) | 85 (8.1) | 1972 (5.6) | ||
Alcohol use | No | 25,862 (76.1) | 766 (72.7) | 26,628 (76.0) | 0.009a |
Yes | 8103 (23.9) | 288 (27.3) | 8391 (24.0) |
Distribution of factors that affect whether individuals experience work accidents.
p < .01
p < .05.
19.2, 14.5, 28.9, 20, and 17.4% of those who experienced a work accident participated in the survey in 2008, 2010, 2012, 2014, and 2016, respectively. In terms of age range, 15.6% of employees who experienced work accidents were between 15 and 24 years old, 30.3% were between 25 and 34 years old, 29.2% were between 35 and 44 years old, 18.7% were between 45 and 54 years old, 4.5% were between 55 and 64 years old, and 1.8% were 65 years and older. In terms of education level, while 5.6% of workers, who had experienced work accidents were illiterate, 44.6% graduated from primary school, 21.4% were secondary school graduates, 20.3% were high school graduates, and 8.1% were university graduates. For occupational groups, while 3.2% of work accident victims were managers, 5.1% belonged to professional occupational groups, 5% were technicians and assistant members of professional occupations, 2.1% are office staff, 12.7% were service/sale staff, 15% were qualified agricultural/forestry/aquaculture workers, 26.9% were artists and related employees, 11.9% were facility-machinery operators/assemblers, and 18.1% were workers in non-qualified jobs.. While 67% of work accident victims had very good health, 26.3% had medium health, and 6.7% had extremely poor health. In addition, 8.1% of work accident victims had received psycho-social support or were depressed. Finally, 27.3% of work accident victims drank alcohol.
According to the chi-square independence test results in Table 1, a significant relationship was found between individuals experiencing work accidents with injury and socio-demographic and economic variables (except marital status and work schedule).
Variance Inflation Factors (VIF) value, β coefficient, standard error, OR value, and confidence intervals related to the binary logistic regression model are shown in Table 2. Before model estimation, the issue of multicollinearity between variables should be investigated. Variables with a VIF value over five caused mid-level multicollinearity, and variables with a VIF value over 10 caused high multicollinearity [24]. As seen in Table 2, no variable in the model has a VIF value of five or above. Accordingly, no variable that causes multicollinearity between variables in the model exists.
Variables | VIF | β | Std. Error | P | OR | 95% CI | |
---|---|---|---|---|---|---|---|
Low. | Up. | ||||||
Survey year (reference category: 2008) | |||||||
2010 | 1.67 | −0.195 | 0.122 | 0.111 | 0.823 | 0.648 | 1.046 |
2012 | 2.09 | −0.128 | 0.104 | 0.219 | 0.880 | 0.717 | 1.079 |
2014 | 1.9 | −0.261 | 0.114 | 0,022b | 0.770 | 0.615 | 0.964 |
2016 | 1.85 | −0.246 | 0.121 | 0,042b | 0.782 | 0.617 | 0.991 |
Gender (reference category: female) | |||||||
Male | 1.2 | 0.809 | 0.107 | 0.000a | 2.246 | 1.822 | 2.769 |
Age (reference category: 15–24) | |||||||
25–34 | 3.25 | −0.229 | 0.134 | 0.088c | 0.795 | 0.611 | 1.035 |
35–44 | 3.90 | −0.509 | 0.148 | 0.001a | 0.601 | 0.450 | 0.803 |
45–54 | 3.39 | −0.721 | 0.160 | 0.000a | 0.486 | 0.355 | 0.665 |
55–64 | 2.19 | −1.202 | 0.215 | 0.000a | 0.300 | 0.197 | 0.458 |
65+ | 1.56 | −1.217 | 0.308 | 0.000a | 0.296 | 0.162 | 0.542 |
Level of education (reference category: university) | |||||||
Did not finish school/illiterate | 1.89 | 0.339 | 0.230 | 0.141 | 1.404 | 0.894 | 2.204 |
Primary school | 3.25 | 0.539 | 0.179 | 0.003a | 1.714 | 1.208 | 2.434 |
Secondary school | 2.16 | 0.441 | 0.182 | 0.016b | 1.554 | 1.087 | 2.222 |
High school | 2.00 | 0.477 | 0.168 | 0.004a | 1.612 | 1.160 | 2.238 |
Marital status (reference category: single) | |||||||
Married | 1.53 | 0.079 | 0.109 | 0.467 | 1.082 | 0.875 | 1.339 |
Work schedule (reference category: part time) | |||||||
Full time | 1.06 | 0.072 | 0.168 | 0.669 | 1.074 | 0.773 | 1.494 |
Occupation (reference category: manager) | |||||||
Professional occupational group worker | 2.87 | 0.440 | 0.267 | 0.100 | 1.553 | 0.920 | 2.624 |
Technician | 1.91 | 0.697 | 0.252 | 0.006a | 2.008 | 1.225 | 3.292 |
Office worker | 1.76 | −0.007 | 0.313 | 0.982 | 0.993 | 0.538 | 1.833 |
Service employee and sale representative | 2.87 | 0.614 | 0.225 | 0.006a | 1.848 | 1.189 | 2.875 |
Qualified agricultural worker | 3.15 | 1.109 | 0.233 | 0.000a | 3.031 | 1.922 | 4.781 |
Artist | 2.78 | 1.452 | 0.218 | 0.000a | 4.270 | 2.786 | 6.542 |
Facility and machinery operator | 2.11 | 1.020 | 0.228 | 0.000a | 2.774 | 1.773 | 4.340 |
Non-qualified job worker | 2.65 | 1.241 | 0.224 | 0.000a | 3.459 | 2.228 | 5.370 |
General health status (reference category: poor) | |||||||
Very good | 4.97 | −0.650 | 0.154 | 0.000a | 0.522 | 0.386 | 0.706 |
Medium | 4.61 | −0.242 | 0.155 | 0.120 | 0.785 | 0.579 | 1.065 |
Psycho-social support/depression (reference category: no) | |||||||
Yes | 1.05 | 0.495 | 0.140 | 0.000a | 1.641 | 1.246 | 2.160 |
Alcohol use (reference category: no) | |||||||
Yes | 1.09 | 0.286 | 0.084 | 0.001a | 1.331 | 1.130 | 1.568 |
Binary logistic regression estimation results of socio-demographic and economic factors that affect whether individuals experience work accidents.
p < .01
p < .05
p < .10.
VIF, variance inflation factor; Std. Error, standard error; Low., lower; Up., upper.
According to the binary logistic regression analysis, when OR < 1, the estimated factor (according to the reference category) had little effect on the investigated state. When OR > 1, it had an increasing effect compared to the reference category [25]. As a result of the analysis, compared to the individuals surveyed in 2008, the odds ratio of individuals who participated in the survey in 2014 (OR = 0.770; 95% CI = 0.615–0.964) and 2016 (OR = 0.782; 95% CI = 0.617–0.991), was lower. In addition, men (OR = 2.246; 95% CI = 1.822–2.769) had higher odds of having a work accident than women. Considering the age variable, compared to the 15–24 group, the age ranges of 25–34 (OR = 0.795; 95% CI = 0.611–1.035), 35–44 (OR = 0.601; 95% CI = 0.450–0.803), 45–54 (OR = 0.486; 95% CI = 0.355–0.665), 55–64 (OR = 0.300; 95% CI = 0.197–0.458) and 65+ (OR = 0.296; 95% CI = 0.162–0.542) had a lower odds ratio of experiencing work accidents.
In terms of educational status, it was seen that primary school graduates (OR = 1.714; 95% CI = 1.208–2.434), secondary school graduates (OR = 1.554; 95% CI = 1.087–2.222), and high school graduates (OR = 1.612; 95% CI = 1.160–2.238) had higher odds ratio of work accident than university graduates. When the occupational groups were examined, technicians/assistant professional members (OR = 2.008; 95% CI = 1.225–3.292), service/sales staff (OR = 1.848; 95% CI = 1.189–2.875), qualified agriculture/forestry/aquaculture workers (OR = 3.031; 95% CI = 1.922–4.781), craftsmen/related workers (OR = 4.270; 95% CI = 2.786–6.542), plant-machine operators/assemblers (OR = 2.774; 95% CI = 1.773–4.340), and those who work in jobs that do not require qualification (OR = 3.459; 95% CI = 2.228–5.370) have higher odds of having a work accident than managers. When general health status is examined, the odds ratio of experiencing work accident of those with very good health (OR = 0.522; 95% CI = 0.386–0.706) is lower than those with poor health status. People who receive psycho-social support/are depressed (OR = 1.641; 95% CI = 1.246–2.160) had higher odds of having a work accident than others. Finally, the odds ratio of experiencing work accidents for participants who used alcohol (OR = 1.331; 95% CI = 1.130–1.568) was higher than for those who did not.
Average direct elasticities and standard errors in the socio-demographic and economic factors that influence whether individuals experience work accidents resulting in injuries in Turkey are provided in Table 3.
Variables | Elasticity (%) | Std. Error |
---|---|---|
Year (reference category: 2008) | ||
2010 | −18.8 | 0.118 |
2012 | −12.4 | 0.100 |
2014 | −25.3b | 0.111 |
2016 | −23.8b | 0.117 |
Gender (reference category: female) | ||
Male | 78.9a | 0.105 |
Age (reference category: 15–24) | ||
25–34 | −22.0c | 0.128 |
35–44 | −49.0a | 0.142 |
45-54 | −69.6a | 0.154 |
55-64 | −116.8a | 0.21 |
65+ | −118.2a | 0.302 |
Level of education (reference category: university) | ||
Did not finish school/illiterate | 33.1 | 0.224 |
Primary school | 52.4a | 0.175 |
Secondary school | 42.9b | 0.178 |
High school | 46.5a | 0.164 |
Marital status (reference category: single) | ||
Married | 7.7 | 0.105 |
Work schedule (reference category: part time) | ||
Full time | 7.0 | 0.163 |
Occupation (reference category: manager) | ||
Professional occupational group worker | 43.4 | 0.263 |
Technician | 68.5a | 0.248 |
Office worker | −0.71 | 0.309 |
Service employee and sale representative | 60.4a | 0.222 |
Qualified agricultural worker | 108.4a | 0.228 |
Artist | 141.2a | 0.214 |
Facility and machinery operator | 99.9a | 0.244 |
Non-qualified Job worker | 121.1a | 0.221 |
General health status (reference category: poor) | ||
Very good | −62.4a | 0.146 |
Medium | −23.1 | 0.148 |
Psycho-social support/depression (reference category: no) | ||
Yes | 47,6a | 0.134 |
Alcohol use (reference category: no) | ||
Yes | 27,6a | 0.081 |
Elasticity estimates for socio-demographic and economic factors that influence whether individuals experience work accidents.
p < .01.
p < .05.
p < .10.
Std. Error, standard error.
For marginal effects, the probability of experiencing work accidents was lower in other years compared to 2008. In terms of gender, the probability of men experiencing work accidents was 78.9% higher than women. Also, as age increased compared to the age range of 15–24, the probability of work accidents decreased. The probability of individuals within the age groups of 25–34, 35–44, 45–54, 55–64, and 65+ are 22%, 49%, 69,6%, 116,8% and 118,2%, respectively, lower than the 15–24 age range.
When the education levels are analyzed, primary school graduates, secondary school graduates, and high school graduates are 52.4%, 42.9%, and 46.5% more likely to have a work accident than university graduates, respectively.
When looking at the occupational groups, the probability of technicians, service/sales staff, qualified agricultural workers, craftsmen, plant/machine operators, and those who do not work in qualified jobs is, respectively, 68.5, 60.4, 108.4, 141, 99.9, and 121.1% higher than managers.
When the general health status is examined, those with very good general health status are 62.4% less likely to have a work accident than those who have poor health. In addition, those who receive psycho-social support/are depressed are 47.6% more likely to have a work accident than other individuals. Those who use alcohol are 27.6% more likely to have a work accident than those who do not.
Work accidents remain important worldwide. Work accidents and diseases influence the whole country economically, socially, and psychologically. 286.068 work accidents occurred in Turkey in 2016. 1405 people died in these work accidents [9]. The loss of these people exerted great pressure on the country, both socially and economically. In addition, even if these accidents did not result in loss of life, the workers being unable to work as a result of their injuries, their inability to continue their work for a long time, or scars they have because of these accidents psychologically depress individuals, apart from economic problems. For this reason, it is of great importance to determine the causes of work accidents and to try to prevent these accidents by concentrating on their causes [6].
The aim of this study was to investigate the factors affecting work accidents of individuals that resulted in injuries in the last 12 months in which the survey was conducted in Turkey. As a result of the analysis, the variables of gender, age, education, occupation, health, psycho-social support/depression, and alcohol use were detected statistically significant.
According to study findings, men have more work accidents than women. Similar results can be found in many studies in the literature [26, 27]. In addition, it was detected in some studies that men are more likely to experience fatal work accidents [28]. This situation can be explained with the fact that men work more in dangerous jobs that require physical power than women.
According to the results of the analysis, it was found that the age range that had the most work accidents was 25–34, while the age range that had the least accidents was 65+. Although the physical activity of workers decreased as they get older, their increased experience was effective in decreasing work accidents with age. In this context, there are many studies showing that work accidents are most common in the 25–44 age range and least common in the 65+ age range [12, 13, 29, 30, 31]. In some studies, the 16–24 age range was found to be the age group where work accidents occurred most frequently [26, 32]. There are also studies indicating that the 35–45 age range is the age group that most frequently experiences fatal work accidents [28].
It was detected that the probability of having a work accident decreases with an increase in the level of education. This may be due to the fact that workers who have a low level of education work in low-profile and risky jobs, or it may be due to individuals having an incomplete understanding risk factors due to a lack of education [31, 33, 34]. In addition, individuals who had not received vocational training were more likely to experience work accidents. Therefore, individuals should undergo specific training before starting to work, and, basic work-related safety measures should be taught [35]. In addition, the fact that individuals did not have sufficient work-related training increased the risk of fatal accidents. One out of every five deaths in construction workers and 95% of the deceased workers were uneducated people [28]. Workers receiving professional training to improve their job competencies and increase their job-related knowledge had an important role in preventing work accidents. In addition, developing a safety culture with training activities and the integration of these activities into corporate culture will make safety a reality at each level [36]. Also, as job safety and health training become more appealing, individuals will receive three times more information, thus considerably reducing work accidents. Applied, student-centered, and participatory training activities should be therefore put into practice [37].
It was detected that individuals working in lower level jobs were more exposed to work accidents. This may arise from the risk and safety awareness of the employees. It is expected that this result arises from the fact that those who work in jobs requiring more strength have generally received less education and people who work in upper-level positions, such as managers, will have a certain awareness, due to their education. There is a strong relationship between safety awareness and risks experienced [38]. For this reason, improving the safety awareness of individuals is of utmost importance. Accordingly, it is vital that individuals receive training in risk management and the use of personal protective equipment [39].
It was detected in the study that people who had received psycho-social support/had experienced depression had more work accidents. It has been demonstrated in several studies that stressful living conditions increase the probability of having a work accident. It has been demonstrated that situations affecting the personal life of individuals, such as being unable to consume adequate food, a suffocating work life, and environmental problems, increase the risk of work accidents [33, 40, 41, 42]. Stress and pressure can have different consequences on the probability of men or women to experience work accidents. While stress caused by a lack of organizational support in women is a major reason for them to experience work accidents, this situation was not applicable to men experiencing work accidents [43]. Accordingly, individuals’ low level of social support from their workplace and stress increased work accidents. It has also been reported in similar studies that women are more affected by workload and stress [44, 45]. However, the fact that individuals experience the pressure of the requirements of high productivity also increases the likelihood of work accidents because individuals can display dangerous behaviors due to this pressure [46]. All these behaviors and stress trigger further depression.
According to the study results, alcohol consumption caused an increased rate of work accidents. There are studies that demonstrate alcohol use and smoking increase work accidents, both in men and women [44]. Contrary to this study, there are also other studies in the literature demonstrating that bad habits such as using alcohol and smoking do not influence work accidents [45]. Additionally, it was found that people with poor health are more likely to experience work accidents than people with good health. In general, people’s poor health conditions make them unable to focus on their job and not being careful enough. This situation leads employees to being exposed to accidents.
Methods such as young employees receiving a good education, workers completing their education before beginning work, regular check-ups for employees and early intervention in diseases, attempts to reduce stress in work life as well as to reduce the negative impact of the job environment on employees, preserving a positive work-life balance, and supporting employees with bad habits such as alcohol and smoking through various rehab activities can play significant roles in reducing work accidents. Also, having first aid experts and doctors constantly available on the job site will help to minimize bad outcome from injuries with early intervention in accidents. In addition, workers not working for long hours during the day, workers having sufficient breaks, and workers having holidays will prevent loss of focus during work, thus playing a great role in reducing work accidents. Using safety signs in workplaces, having constant supervision of employees during working hours, and immediate intervention with people who violate safety rules will have a minimizing impact on work accidents.
This study had several limitations. First, the study data were secondary data. Variables required for statistical analysis consisted of existing variables in the dataset. Second, as the data was cross-sectional, a definitive causal relationship on factors that influence work accidents could not be inferred. Third, this study was not based on recorded data. The data were collected through surveys conducted by the TSI. The data obtained in this study were direct responses from individuals. Since there is no officially recorded data, results obtained from the data collection method could be biased. Fourth, since the data was collected via a survey and actively answered by working individuals, it did not contain data related to fatal accidents. Finally, the frequency of alcohol consumption for individuals who drank alcohol could not be determined.
The authors would like to thank the Turkish Statistical Institute for the data. The views and opinions expressed in this manuscript are those of the authors only and do not necessarily represent the views, official policy, or position of the Turkish Statistical Institute.
The authors declare no conflict of interest.
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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I received a B.Eng. degree in Computer Engineering with First Class Honors in 2008 from Prince of Songkla University, Songkhla, Thailand, where I received a Ph.D. degree in Electrical Engineering. My research interests are primarily in the area of biomedical signal processing and classification notably EMG (electromyography signal), EOG (electrooculography signal), and EEG (electroencephalography signal), image analysis notably breast cancer analysis and optical coherence tomography, and rehabilitation engineering. I became a student member of IEEE in 2008. During October 2011-March 2012, I had worked at School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, United Kingdom. In addition, during a B.Eng. 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