Introduction
The perception of an occupational risk refers to the probability of a potential loss of some part of the body or harm to a person’s health as a result of exposure to risk factors (Sjöberg, 2000); i.e., the worker's ability to perceive the existence or not of risk factors that may cause accidents (Cezar-Vaz et al., 2012). Understanding the relationship between risk perception, knowledge and personal protective behaviors could play a major role in occupational risk control and management (Thepaksorn, Siriwong, Neitzel, Somrongthong, & Techasrivichien, 2018).
Workplace risk factors vary by sector and company scale. In the forest sector, wood processing activities are considered unhealthy (Sobieray, Nogueira, Durante, & Lambert, 2007) and risky because they are carried out under high pressure, an accelerated pace, temperature extremes and high noise and wood dust levels (Michael & Wiedenbeck, 2004). This results in prolonged exposure to risk factors that, if not addressed in a timely manner, risk the physical integrity of workers and cause an increasing number of accidents or illnesses (Top, Adanur, & Öz, 2016).
Sawmill workers are exposed to biological, physical and ergonomic hazards that can lead to disease. Among biological hazards, wood dust (a well-known carcinogen), microorganisms, endotoxins, resin acids, and vapors containing terpenes can cause skin irritation, allergies, and respiratory problems such as asthma, chronic bronchitis, rhinitis, and reduced lung function (Straumfors et al., 2018). Among the physical hazards, occupational noise is a common harmful agent and one of the most important risk factors to consider, especially after a prolonged period of exposure. Hearing loss due to noise is an occupational disease of high incidence in workers in mechanized industries such as sawmills; possible effects include acoustic trauma, tinnitus, temporary or permanent threshold shift, and interference with communication (Otoghile, Onakoya, & Otoghile, 2018). Slips, trips and falls are also an important class of incidents that cause death or injury in sawmills.
The wood products industry has high rates of acute and chronic injuries to workers due to ergonomic risk factors generated from the handling of heavy loads and repetitive movements, resulting in musculoskeletal disorders or overexertion (Institute of Medicine and National Research Council [IMNRC], 2001); however, there is limited evidence on preventable risk factors for these injuries (Holcroft & Punnett, 2009).
Occupational risk assessment is a structured and systematic process that depends on the correct identification of potentially dangerous factors and agents in the workplace (Carneiro, Alves, Rodrigues, Levy, & Sordi, 2018), and on the worker's attitude and behavior regarding his or her own perception of risk, which does not underestimate safety itself (Corrao, Mazzotta, La Torre, & De Giusti, 2012). For an accident to occur, unsafe acts and unsafe conditions are needed, so the perception of risk is a good predictor of occupational safety (Oppong, 2015).
In Mexico, the level of safety awareness among forest industry manufacturing workers has not been documented. In this regard, the present study measured the level of workers' perception of the most common risk factors in job performance, such as exposure to noise, lighting, dust, ambient temperature and vibrations, and identified the use and non-use of personal protective equipment in 11 sawmills in the El Salto forest region, Pueblo Nuevo, Durango. This was done in order to contribute to improving occupational health and minimizing exposure to risk factors in the forest industry.
Materials and methods
Study area
The study was conducted in the municipality of Pueblo Nuevo, state of Durango, Mexico, in 11 sawmills that carry out the lumber production process. The list of jobs evaluated by sawmill is presented in Table 1. Operators are workers who hold a more specialized position, where decision-making at work is a priority. This segment includes chainsaw operators, sawyers, and pendulum, trimmer, edger, bandsaw, resaw and forklift operators. Operator assistants are the workers who receive direct indications from the main operator of each type of machinery, while the general assistants are the people who are mainly responsible for cleaning, order, distribution and arrangement of raw and other materials.
Methods
A structured questionnaire was applied to each of the workers involved in the production process in each sawmill evaluated. The following personal information was included in the questionnaire: a) weight and height (obtained directly from each worker), age, work experience, and history of accidents, illnesses or injuries that occurred while performing their tasks; b) habit in the use of personal protective equipment (PPE) for head, eyes, face, ears, respiratory system, upper extremities, trunk and lower extremities; and c) perception of work area safety and the risk of exposure to noise, lighting, smoke, dust and vibrations in their workstations. Qualification criteria for occupational risk levels were obtained from regulatory standards NOM-008-STPS-2013 (Secretaría del Trabajo y Previsión Social [STyPS], 2013) and NOM-017-STPS-2008 (STyPS, 2008), and the literature review focused on the impact of the main occupational hazards on workers' health (Reinhold & Tint, 2009).
Statistical analysis
With the collected data, cross or contingency tables were prepared using descriptive statistics (mean, standard deviation, frequency and proportion to summarize the sociodemographic variables) and non-parametric inferential statistics (Chi-square tests of association and independence to test the degree of relationship between two categorical variables) (Janicak, 2007). For this, both the asymptotic method and Fisher’s exact test were used, provided that more than 20 % of the expected frequencies had values lower than 5 (Sharpe, 2015). The significance level was set at 5 %. The dependent variable (use of PPE) was related to independent variables (age, work experience, schooling, job position and sawmill) and the type of job position was related to cognitive risk factors (health problems or accidents occurring during work performance, parts of the body affected, perception of factors affecting the worker's health in his job position, perception of workplace safety, parts of the body most exposed to accidents, level of lighting, noise, vibrations, thermal comfort, as well as exposure to smoke and sawdust). The data were analyzed using the SPSS statistical package version 19 (IBM Corp. Released, 2010).
Results and discussion
General information on the profile of sawmill workers
Within the socio-demographic characteristics of the sawmills in the El Salto region of Durango, it was found that the average worker is a 37-yearold man with 15 years of work experience, which suggests that the age for starting work at a sawmill is around 22 years old. All workers are male; 74 % reported being married, 21 % single and the rest separated. Regarding the workplace, machine and equipment operators are the oldest and most experienced (42 and 19 years, respectively). The average schooling level was 6.7 years, corresponding to the sixth grade of primary education.
In the sawmill industry, there is a need for personnel who are physically capable of carrying out very demanding work; therefore, the worker’s weight is of paramount importance, as excess weight poses serious health risks. Weight is a good indicator of a person's ability to undertake physical tasks, but it is even better in indices combined with height, such as the body mass index (BMI) (Rodríguez-Trejo, Santillán-Pérez, & Tchikoué-Maga, 2006). The results indicated that the workers' BMI is equivalent to a general condition of excess weight, implying that, in addition to occupational hazards, their health is exposed to other factors that limit their life expectancy and quality of life.
The decrease in physical activity, a product of a sedentary lifestyle, and the effect of greater automation of work activities, partly explain the workers’ excess weight (Moreno, 2012). The exception to this condition is found in most of the operator assistants who are classified as having a normal or average health risk (Table 2). Some epidemiological studies have identified low educational and income levels as factors associated with excess weight and obesity (Álvarez-Castaño, Goez-Rueda, & Carreño-Aguirre, 2012; Moreno-Altamirano, García-García, Soto-Estrada, Capraro, & Limón-Cruz, 2014).
Variable | Total | Mean | Standard deviation (±) | Minimum | Maximum |
---|---|---|---|---|---|
Workers at the evaluated sawmills | |||||
Age (years) | 108 | 37.02 | 12.69 | 18.00 | 69.00 |
Height (m) | 1.72 | 0.07 | 1.50 | 1.90 | |
Weight (kg) | 74.93 | 11.08 | 47.00 | 103.00 | |
Body mass index (kg·m-2) | 25.45 | 3.39 | 16.98 | 35.64 | |
Classification* | Excess weight (increased risk of disease) | ||||
Work experience (years) | 15.42 | 11.09 | 0.50 | 39.00 | |
Schooling (years) | 6.70 | 1.31 | 3.00 | 11.00 | |
Machine and equipment operators | |||||
Age (years) | 50 | 42.44 | 12.32 | 20.00 | 69.00 |
Height (m) | 1.71 | 0.06 | 1.57 | 1.87 | |
Weight (kg) | 76.94 | 9.01 | 56.00 | 103.00 | |
Body mass index (kg·m-2) | 26.23 | 3.15 | 19.38 | 35.64 | |
Classification* | Preobese (with risk of disease) | ||||
Work experience (years) | 19.44 | 10.48 | 1.00 | 39.00 | |
Schooling (years) | 6.44 | 1.07 | 3.00 | 9.00 | |
Operator assistants | |||||
Age (years) | 35 | 31.80 | 11.66 | 18.00 | 61.00 |
Height (m) | 1.70 | 0.08 | 1.50 | 1.85 | |
Weight (kg) | 68.91 | 8.95 | 47.00 | 92.00 | |
Body mass index (kg·m-2) | 23.94 | 2.92 | 16.98 | 29.30 | |
Classification* | Normal (with normal risk of disease) | ||||
Work experience (years) | 10.98 | 9.34 | 0.67 | 30.00 | |
Schooling (years) | 7.17 | 1.64 | 3.00 | 11.00 | |
Assistants | |||||
Age (years) | 23 | 33.17 | 10.50 | 18.00 | 54.00 |
Height (m) | 1.75 | 0.08 | 1.60 | 1.90 | |
Weight (kg) | 79.70 | 14.23 | 50.00 | 100.00 | |
Body mass index (kg·m-2) | 26.06 | 3.90 | 17.30 | 32.87 | |
Classification* | Preobese (with risk of disease) | ||||
Work experience (years) | 13.46 | 12.12 | 0.50 | 39.00 | |
Schooling (years) | 6.74 | 1.10 | 3.00 | 9.00 |
*Source of classification based on body mass index (World Health Organization [WHO], 2000).
Use of personal protective equipment at sawmills
This study showed that most workers do not use PPE when operating machinery or performing work that requires its use, perhaps because this practice has not been part of their training stages. Therefore, it is not surprising that workers have problems adapting to safety practices, especially the use of clothing and equipment; correcting these habits may require time, training, monitoring and awareness techniques (Ogundipe et al., 2018). The personal protection level of workers in the El Salto region’s sawmills is very low, as 29 % use PPE frequently, 27 % use it sometimes, 23 % rarely and only 21 % always use it; of the last group, 9 % corresponds to workers in the 25-34 age range. It is worth mentioning that a significant association was found between PPE use and worker age (P = 0.0413). Regarding work experience, the segment of workers with more than 10 years of seniority (11 %) are those who always use PPE. According to the level of schooling, 15 % of the workers, who have secondary education, report that they always use PPE, while, by work category, 10 % of the machine and equipment operators always use it. With respect to sawmills, 16 % of the workers at the La Victoria ejido facility make constant use of PPE; the relationship of PPE use with sawmills was highly significant (P = 0.0001) (Table 3).
Category | Total (n = 108, %) | Use of personal protective equipment (%) | P* | |||
---|---|---|---|---|---|---|
Always | Frequently | Sometimes | Rarely | |||
Age (years) | ||||||
<25 | 23 (21.3) | 4 (3.7) | 7 (6.5) | 6 (5.6) | 6 (5.6) | 0.0413* |
25 to 34 | 27 (25.0) | 10 (9.3) | 2 (1.9) | 8 (7.4) | 7 (6.5) | |
35 to 44 | 33 (30.6) | 8 (7.4) | 14 (13.0) | 5 (4.6) | 6 (5.6) | |
>45 | 25 (23.1) | 1 (0.9) | 8 (7.4) | 10 (9.3) | 6 (5.6) | |
Work experience (years) | ||||||
<1 | 5 (4.6) | 1 (0.9) | 1 (0.9) | 1 (0.9) | 2 (1.9) | 0.9220 |
1 to 5 | 23 (21.3) | 5 (4.6) | 5 (4.6) | 8 (7.4) | 5 (4.6) | |
5 to 10 | 20 (18.5) | 5 (4.6) | 4 (3.7) | 5 (4.6) | 6 (5.6) | |
>10 | 60 (55.5) | 12 (11.1) | 21 (19.4) | 15 (13.9) | 12 (11.1) | |
Schooling level | ||||||
Primary | 29 (26.8) | 3 (2.7) | 7 (6.4) | 12 (11.1) | 7 (6.4) | 0.4009 |
Secondary | 66 (61.1) | 16 (14.8) | 19 (17.6) | 15 (13.9) | 16 (14.8) | |
High school | 11 (10.1) | 4 (3.7) | 4 (3.7) | 2 (1.9) | 1 (0.9) | |
Higher education | 2 (1.9) | 0 (0.0) | 1 (0.9) | 0 (0.0) | 1 (0.9) | |
Job position | ||||||
Equipment operator | 50 (46.3) | 11 (10.2) | 13 (12.0) | 16 (14.9) | 10 (9.2) | 0.6070 |
Operator assistant | 35 (32.4) | 6 (5.5) | 12 (11.1) | 6 (5.5) | 11 (10.1) | |
General assistant | 23 (21.3) | 6 (5.5) | 6 (5.5) | 7 (6.5) | 4 (3.7) | |
Sawmill | ||||||
La Victoria | 17 (15.7) | 6 (5.6) | 8 (7.4) | 3 (2.8) | 0 (0.0) | 0.0001* |
El Diamante | 9 (8.3) | 0 (0.0) | 5 (4.6) | 2 (1.9) | 2 (1.9) | |
Aserradero García | 8 (7.4) | 0 (0.0) | 0 (0.0) | 5 (4.6) | 3 (2.8) | |
Quintana I | 10 (9.2) | 0 (0.0) | 1 (0.9) | 4 (3.7) | 5 (4.6) | |
Pueblo Nuevo | 5 (4.6) | 0 (0.0) | 3 (2.8) | 1 (0.9) | 1 (0.9) | |
Quintana II | 10 (9.2) | 0 (0.0) | 3 (2.8) | 1 (0.9) | 6 (5.5) | |
La Peña | 14 (12.9) | 8 (7.4) | 4 (3.7) | 2 (1.9) | 0 (0.0) | |
El Potro | 9 (8.3) | 3 (2.8) | 1 (0.9) | 2 (1.9) | 3 (2.8) | |
PROMADESA | 8 (7.4) | 1 (0.9) | 4 (3.7) | 3 (2.8) | 0 (0.0) | |
Gil Meza | 8 (7.4) | 1 (0.9) | 0 (0.0) | 5 (4.6) | 2 (1.9) | |
San Francisco | 10 (9.2) | 4 (3.7) | 2 (1.9) | 1 (0.9) | 3 (2.8) |
* Statistically significant relationship (Chi-square test, P < 0.05) between the use of personal protective equipment and the category evaluated.
During the observation period, it was possible to visually verify, on some occasions, the use of a helmet and safety goggles to protect the head and eyes; likewise, the sporadic and almost null use of ear and respiratory apparatus protectors was evident. Only the use of safety gloves was observed in an almost generalized manner, but the protection of the lower limbs and torso was inconsistent, because neither the footwear nor the body protection indicated to guarantee protection against possible accidents was used.
Among the causes of non-use of PPE are inadequate supply by management, discomfort in use, custom and low demand for use. This is consistent with the findings of Top et al. (2016), who assessed PPE use among workers in the wood-products manufacturing sector in Turkey, and reported that the most used PPE items in sawmills in Gumushane province were gloves and the least used were hearing protectors, googles, work shoes and dust masks; the most common reason was that PPE causes discomfort while working. Mitchual, Donkoh, and Bih (2015) argue that the low use of dust masks can be attributed to the belief that sawdust generated during log processing is less harmful, as wood is a natural product. There is also a belief that using PPE impedes production and productivity, as it increases the time workers need to perform routine tasks (Irizarry, Simonsen, & Abraham, 2005).
Low PPE use could be partly explained by the argument of Dos Santos-Hurtado de Mendoza and de Mendoza-Borges (2016) and Taha (2000), who state that sawmill workers are generally men from rural areas, with low purchasing power and a low schooling level, making the awareness-raising process within companies difficult. Osonwa-Kalu, Eko-Jimmy, and Ozah-Hosea (2015) also add the lack of knowledge of labor employers regarding their obligation to have PPE in the workplace. Whatever the reason for which workers do not use PPE, all occupational accidents resulting from this behavior are due to an unsafe act that creates unsafe conditions (Oppong, 2015).
It is important to mention that the schooling level of the workers in this study is not a major factor in not knowing the benefits of using PPE, as it was found that all of them understand health and safety notices, posters and signs to avoid hazards in the workplace. The consequences of this omission are related to the fact that most accidents and injuries, associated with the sawmill industry, are caused by the non-use of PPE (Kwame, Kusi, & Lawer, 2014). According to Bello and Mijinyawa (2010), workers who often use PPE generally acquired this habit on the basis of having experienced some injuries and accidents at work, but it is evident that, in general, an adequate approach to personal safety is lacking. Therefore, each worker needs to increase his level of awareness to use PPE, depending on the potential accident hazard sources and the means of protection available.
Perception of risk factors in job positions
Health problems and accidents suffered by machine and equipment operators include, for the most part, injuries to the waist and hands (15 and 18 %, respectively), which coincides with the operators’ perception of those parts of the body most exposed to workplace injury (11 % waist and 18 % hands). Hand injuries correspond to wounds with rotating cutting devices, amputation and crushing of fingers and hands; workers' waist pain and injuries are mainly due to uncomfortable positions and the lifting of heavy loads while performing their tasks. In general, workers also indicated that exposure to equipment noise and vibrations is a high risk factor affecting their health, but few use appropriate protective devices to minimize these risks.
The perception of workplace safety is from fair to good. Regarding the lighting level, the perception that it is good predominated, while the noise level is perceived from high to very high and the vibrations from medium to strong; only the risk perception related to the exposed parts of the body and noise depend on the job position (P < 0.05). Thermal comfort was mainly perceived as pleasant to hot and the amounts of smoke and dust are perceived as excessive (Table 4). This contrasts with the non-perception of dust as a risk factor for health, although it has been reported that total suspended particles in sawmill environments are high and that those from 1 to 5 μm could cause varying degrees of lung deterioration. It is therefore urgent to introduce the use of masks among workers most exposed to sawdust (Adeoye et al., 2014), in this case machine operators and their assistants.
Category | Job position (n = 108) | P* | ||
---|---|---|---|---|
Equipment operator | Operator assistant | General assistant | ||
Health problems or accidents occurring in the course of work (n, %) | ||||
Yes | 20 (18.5) | 14 (13.0) | 6 (5.5) | 0.4718 |
No | 30 (27.8) | 21 (19.4) | 17 (15.7) | |
Parts of the body affected (n, %) | ||||
Arms | 3 (7.5) | 2 (5.0) | 2 (5.0) | 0.3045 |
Waist | 6 (15.0) | 6 (15.0) | 1 (2.5) | |
Shoulders | 2 (5.0) | 0 (0.0) | 0 (0.0) | |
Hands | 7 (17.5) | 6 (15.0) | 1 (2.5) | |
Feet | 2 (5.0) | 0 (0.0) | 2 (5.0) | |
Perception of factors affecting worker's health in his job position (n, %) | ||||
Vibrations | 10 (9.2) | 5 (4.6) | 1 (0.9) | 0.5611 |
Noise | 13 (12.0) | 15 (13.9) | 7 (6.5) | |
Excess work | 3 (2.8) | 1 (0.9) | 3 (2.8) | |
Ambient temperature | 3 (2.8) | 2 (1.9) | 1 (0.9) | |
Bad postures | 16 (14.8) | 10 (9.2) | 6 (5.5) | |
Dust and smoke | 4 (3.7) | 2 (1.9) | 4 (3.7) | |
Lack of equipment maintenance | 1 (0.9) | 0 (0.0) | 1 (0.9) | |
Perception of job safety (n, %) | ||||
Optimal | 9 (8.3) | 4 (3.7) | 1 (0.9) | 0.4367 |
Good | 18 (16.6) | 15 (13.9) | 7 (6.4) | |
Fair | 21 (19.4) | 15 (13.9) | 15 (13.9) | |
Poor | 2 (1.9) | 1 (0.9) | 0 (0.0) | |
Perception of the part of the body most exposed in the workplace (n, %) | ||||
Waist | 12 (11.1) | 3 (2.9) | 7 (6.5) | 0.0143* |
Hands | 19 (17.6) | 12 (11.1) | 2 (1.9) | |
Head | 6 (5.5) | 9 (8.3) | 5 (4.6) | |
Arms | 9 (8.3) | 11 (10.1) | 4 (3.7) | |
Legs | 4 (3.7) | 0 (0.0) | 4 (3.7) | |
Feet | 0 (0.0) | 0 (0.0) | 1 (0.9) | |
Perception of the lighting level in the workplace (n, %) | ||||
Very good | 6 (5.5) | 6 (5.5) | 5 (4.6) | 0.2557 |
Good | 34 (31.5) | 27 (25.0) | 17 (15.7) | |
Satisfactory | 8 (7.4) | 2 (1.9) | 0 (0.0) | |
Sufficient | 2 (1.9) | 0 (0.0) | 1 (0.9) | |
Perception of the noise level in the workplace (n, %) | ||||
Very high | 14 (12.3) | 17 (15.7) | 3 (2.8) | 0.0209* |
High | 20 (18.5) | 12 (11.1) | 8 (7.4) | |
Moderate | 16 (14.9) | 6 (5.5) | 12 (11.1) | |
Perception of vibrations in the workplace (n, %) | ||||
Very strong | 12 (11.1) | 7 (6.5) | 1 (0.9) | 0.1787 |
Strong | 17 (15.7) | 7 (6.4) | 9 (8.3) | |
Medium | 17 (15.7) | 16 (14.8) | 8 (7.4) | |
Low | 3 (2.8) | 4 (3.7) | 2 (1.9) | |
Nonexistent | 1 (0.9) | 1 (0.9) | 3 (2.8) | |
Perception of thermal comfort in the workplace (n, %) | ||||
Very hot | 19 (17.6) | 12 (11.1) | 10 (9.2) | 0.6493 |
Hot | 14 (13.0) | 14 (13.0) | 8 (7.4) | |
Pleasant | 13 (12.0) | 9 (8.3) | 5 (4.6) | |
Cold | 2 (1.9) | 0 (0.0) | 0 (0.0) | |
Very cold | 2 (1.9) | 0 (0.0) | 0 (0.0) | |
Perception of the amount of smoke and dust in the workplace (n, %) | ||||
Excessive | 36 (33.3) | 30 (27.8) | 20 (18.5) | 0.4779 |
Medium | 10 (9.2) | 4 (3.7) | 2 (1.9) | |
Little | 4 (3.7) | 1 (0.9) | 1 (0.9) |
*Statistically significant relationship between job type and risk factors (Chi-square test, P < 0.05).
Workers remain standing for several hours, a common situation in sawmills where most workers acquire forced postures that invariably generate musculoskeletal injuries, due to a high application of force in twisting, pushing, pulling, stretching or flexing movements and repetitive movements (Thepaksorn et al., 2017). This was observed in the general assistants, who move very heavy materials such as logs and recently sawn wood that they distribute in the yards, rolling the logs and carrying the sawn wood on their shoulders for distances of 3 to 30 m. In addition, prolonged exposure to machine vibrations and noise causes the constriction of blood vessels in the hands and arms, reducing the blood supply to the fingers and hands, making them insensitive due to numbness, bleaching and tingling that make them prone to some mechanical damage. On the other hand, noise above 85 dB could lead to injury or hearing loss; in addition, it interferes with communication at work, which could lead to accidents (Qutubuddin, Hebbal, & Kumar, 2012).
In the present study, the workers most susceptible to these disorders are machine operators and their assistants, so resorting to mandatory rest periods could reduce the effect on affected workers (Adeyemi & Udoh, 2016). Other non-visible body effects are changes in environmental and body temperature in combination with noise, as they could increase blood pressure and pulse rate triggering stress in workers (Bello & Mijinyawa, 2010; Qutubuddin et al., 2012). It is also important to mention that because this study was conducted in the months of April to June, the environmental temperature records in the shade were up to 34 °C, which explains the workers' perception that their work area is hot. This high ambient temperature thermal factor, together with direct sunlight radiation and the physical load associated with the work, induces heatstroke symptoms, so frequent hydration is necessary under these conditions (Maeda et al., 2006).
The workers stated that their perception of workplace safety is from fair to good, but direct observation of each post revealed that safety, organization, and safe work procedures are not the most adequate, due to the fact that in some areas there is an accumulation of wood chips, strips, shavings and sawdust that obstructs the worker's free movement. Risk perception is related to job position and to the needs for PPE use. Lombardi, Verma, Brennan, and Perry (2009) suggest that the workers’ risk perception is due to the nature and duration of the task, where shorter duration and non-repetitive tasks are generally not considered high risk and therefore they do not use PPE. These authors add that these brief tasks are associated with very high-risk periods and contribute to a high frequency of injuries; furthermore, they mention that the worker’s age also influences risk perception, as younger and inexperienced workers are less likely to perceive the risk of injury and are less prone to use PPE. This was evident in the present study where workers under 25 are the segment of the working population that uses PPE the least, so targeting safety programs at younger workers can be effective in increasing their risk perception.
Another important aspect to consider is simulation, as some workers only use PPE because of the company's compliance policy or when they feel observed; in this case, they are very unlikely to use PPE continuously (Arezes & Miguel, 2005). Finally, Oppong (2015) states that there are workers who look for risks, avoid them or are indifferent, and who also have an objective or bearable level of risk with which they feel comfortable; instead, when they find themselves in situations that cause variation in the level of risk, they adjust their behavior to move to a more comfortable level. Therefore, employers should not hire people who like to take risks or who look for them because they are the most accident-prone.
Conclusions
The average sawmill worker in the El Salto region of Durango is a mature male, with sufficient work experience, who is exposed to risks that affect his life expectancy and quality of life. Staff do not use full personal protective equipment during the workday, only gloves. They perceive noise and vibrations as the most serious risk factors affecting their health, but not sawdust, and they perceive the safety of their workplace as being from fair to good. Based on this observation, it is recommended that greater attention be paid to supervising workers for the proper use of personal protective equipment in their work environment, and to training them on aspects of safety and hygiene at work in order to increase the level of perception of the risks associated with their functions.