Musculoskeletal disorders,personality traits,psychological distress,and accident proneness of Chinese coal miners

Abstract

BACKGROUND:

Human factors comprise one of the important reasons leading to the casualty accidents in coal mines.

OBJECTIVE:

The aim of this study was to analyze the relationships among musculoskeletal disorders (MSDs), personality traits, psychological distress, and accident proneness of coal miners.

PARTICIPANTS:

There were 1500 Chinese coal miners surveyed in this study. Among these miners, 992 valid samples were obtained.

METHODS:

The study surveyed the MSDs, personality traits, psychological distress, and accident proneness of coal miners with MSDs Likert scale, Eysenck personality questionnaire, Symptom Checklist-90 (SCL-90) scale, and accident proneness questionnaire, respectively.

RESULTS:

The highest MSDs level was found in the waist. The increasing working age of the miners was connected with increased MSDs and psychological distress. Significant differences in MSDs and psychological distress of miners from different types of work were observed. Coal miners with higher MSDs had higher accident proneness. Coal miners with higher neuroticism dimension of Eysenck personality and more serious psychological distress had higher accident proneness. Phobic anxiety, paranoid ideation and psychoticism dimension of psychological distress were the three most important indicators that had significant positive relationships with accident proneness.

CONCLUSION:

The MSDs, neuroticism dimension, and psychological distress of the coal mine workers are important to work safety and require serious attention. Some implications concerning coal mine safety management in China were provided.

Keywords

Coal mine safety physiological health personality traits psychological distress

1 Introduction

Coal mining is a high-risk business, in which many devastating accidents occurred worldwide [1, 2]. Due to the complicated production process and the harsh conditions in coal mines, the problem of safety production is significant, complicated, and difficult to solve compared with other industries [3]. Torma-Krajewski et al. [4] reported the effect of implementing an ergonomic process in a coal mine in the US and demonstrated that the ergonomics process can be effectively implemented in an industry with dynamic work conditions and environments.

China has a high record of coal mine accidents. The State Administration of Coal Mine Safety reported that 47,875 people died in 28,868 coal mine accidents that occurred between 2001 and 2011 [5]. Human factors comprise one of the important reasons leading to the casualty accidents in coal mines [5, 6]. In recent years, the Chinese government and the coal mine enterprises have paid great attention to human safety in order to improve the safety situation of coal mines.

In the coal mines, the physiological and psychological states of the miners are influenced by the heavy work and hostile environment [5 , 7–9]. In China, many coal miners acquire musculoskeletal disorders (MSDs) and have not received great attention. Moreover, miners who work in coal mines for a longer time tend to be more restless and tired, which can result in higher accident proneness. It has been reported that human physiological and psychological states may influence work performance, and physiological and psychological data have successfully achieved the most productive use of human capabilities as well as the maintenance of human health and well-being [10]. Thus, the physiological and psychological states of coal miners may have a certain relationship to their accident proneness.

In the past 30 years, the problem of work-related musculoskeletal disorders (MSDs) has become a growing concern in industrialized countries [11], and the occurrence of MSDs in the workplace has remained a considerable problem [12 –14]. Levanon et al. [12] utilized the MSDs questionnaire and evaluated the efficacy of a workplace intervention for reducing MSDs among computer workers. Barzideh et al. [13] employed the MSDs questionnaire to analyze the relationship between MSDs and job stress among Iranian nurses. Yu et al. [14] used the Ovako Working Posture Analysis System (OWAS) and behavior observation to analyze MSDs risk factors involved in the power line fixing work in China. Aptel et al. [15] adopted a case study to analyze a workstation rotation system which aimed to reduce the risk of MSDs. Meanwhile, EI-Bestar et al. [16] employed the MSDs questionnaire and an X-ray of the neck to study work-related risk factors of neck-upper MSDs among video display terminal (VDT) users. Torma-Krajewski et al. [17] analyzed the risk factors of miners and concluded that the most frequently reported risk factor exposure is repetition, followed by heavy lifting, forceful gripping, poor posture, and bouncing. In their study they used a discomfort survey of body parts of miners and found that the most frequently reported body parts with discomfort are the lower back and hands (wrists), followed by the shoulders, neck, upper back, and elbows. However, to the best of our knowledge, no study has yet paid attention to MSDs acquired by coal miners, especially Chinese coal miners. The reason may be that in existed studies the life safety of coal miners is paid much more attention than the physical health of coal miners.

Some instruments have been designed to measure individual personality traits. One of the most popular is the Eysenck Personality Questionnaire (EPQ) [18]. In some studies, EPQ was used to investigate the relationship between personality and accident [19 –21]. EPQ was developed in England by Eysenck [18], and was widely used in many English-speaking countries (e.g. Irish [22]), and non-English speaking countries (e.g. China [23] and Spain [24]). EPQ has three dimensions, namely, neuroticism (N), psychoticism (P) and extraversion (E). Chu et al. [19] analyzed the relationship between work injuries of coal miners and their personality traits and mental health, and concluded that higher neuroticism and extraversion are high risk factors of work injuries. Li et al. [20] measured pilots with EPQ and found that neuroticism dimension and the ages of pilots can be used in predicting the accident proneness of pilots.

Psychological distress is the result of many factors, including psychogenic pain, internal conflicts and external stress, which prevent a person from achieving self-actualization and establishing connections with “significant others” [25]. In relation to these, the Symptom Checklist-90 (SCL-90) has been widely explored in the assessments of psychological distress [8 , 27]. Song et al. [8] employed SCL-90 in analyzing the overall mental health level of coal miners and found that such miners, especially underground workers, have mental health problems, including somatization, obsessive-compulsive, interpersonal sensitivity and hostility dimension, which are all serious and worrisome. Chu et al. [19] used SCL-90 to analyze the relationship between work injury and mental health of coal miners, and found that the depression dimension of SCL-90 is high-risk factor of work injury.

Accident proneness theory is the first theory that systematically investigates the possibility of individuals getting into accidents while working within a specific system. The theory suggests the existence of a certain subgroup within the general population whose members are more likely to meet accidents [28]. The concept of accident proneness posits the idea that some people might have predisposition, more likely to suffer accidents than others [29 –31]. Li et al. [20] verified the validity of predicting accident proneness of pilots with EPQ and illustrated that the neuroticism dimension of the pilots helped predict the accident proneness of the pilots. Rani and Chaturvedula [32] analyzed the accident proneness of 615 pilots in India and found that flying aptitude is related to accident proneness, implying that the intelligence and personality of an individual should be considered during pilot selection. Gauchard et al. [33] employed a case-control study to analyze the accident proneness of railway workers, and identified some work and individual factors, such as sleep disorders and smoking, which predicted occupational injury frequency. Maiti [34] conducted a case study on coal miners and the results show that older miners were more injury prone than their younger counterparts and the risk index of the face miners was higher compared to the other miners.

To sum up, the study on human factor safety and the related influencing factors, especially for Chinese coal miners, has only just started. To further improve coal mine safety management, it is essential to investigate the physiological and psychological states, and accident proneness of Chinese coal miners. In the current paper, the MSDs, personality traits, psychological distress and accident proneness of 1500 coal workers from a coal mine in China are measured and analyzed. Some implications are then provided for coal mine safety management in China based on the findings.

2 Method

2.1 Participants

A total of 1500 coal mine workers from Sichuan Province Coal Mine Company in China participated voluntarily in this study, in which the questionnaires were completed individually. All 1500 coal miners were male. Participants provided their working age (in years) and type of work. For “type of work”, there were nine items of mining activity to choose from as follows: digging, gas detection, carriage, coal mining, water detection, blasting miner assistant, blasting miner, electromechanical miner and miner with other types of work. Some research assistants helped the participants if they had any questions related to the questionnaires. After the survey, each participant was given a souvenir for their participation. In total, 1105 questionnaires were collected. Among these, 113 questionnaires were rejected because the data was incomplete. In the end, 992 valid samples were obtained.

2.2 Instrument

The survey used an anonymous questionnaire in a paper-and-pen format. The questionnaires consisted of four parts, namely, MSDs Likert scale, Eysenck Personality Questionnaire, Symptom Checklist-90 scale, and Accident Proneness questionnaire.

2.2.1 MSDs Likert scale

This study adopted the MSDs Likert scale, which focused on eight body parts, namely, neck, shoulders, back, waist, hands (wrists), legs, knees and feet (ankles). Each item had five levels ranging from 1 to 5 (1 means not at all, 5 means extremely). Higher level scores depicted more serious MSDs of the body part of the respondent. The MSDs of the whole body, was the sum of the MSDs of all eight body parts.

2.2.2 Eysenck Personality Questionnaire (EPQ)

The EPQ 1983 version of Chen ZG was already tested in Chinese for reliability and validity [35]. Hence, this Chinese EPQ version was adopted in this study. EPQ was used here to measure the extraversion, emotionality and adventurousness scores of the participants. Chu et al. [19] and Li et al. [20] reported that the neuroticism dimension of Eysenck personality was one of the high risk factors of work injury or accident proneness. The EPQ had 88 questions, which could be answered with a “yes” or a “no”. The total score was the sum of all “yes” answers, and higher scores indicated higher Eysenck personality results of the participants.

2.2.3 Symptom Checklist-90 (SCL-90) scale

The SCL-90 scale [27] was adopted in this study to mearsure somatization, obsessive-compulsive, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, and psychoticism scores of the participants. Several studies have demonstrated the reliability, validity, and utility of the instrument, which is one of the widely used measures of psychological distress in practice and research [8 , 27]. The SCL-90 scale consists of 90 items, which yields nine scores along primary symptom dimensions, such as somatization, obsessive-compulsive, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, and psychoticism. Each question had levels ranging from 1 to 5. A higher score indicated higher level of seriousness of the symptom specified. The sum of the scores of all 90 questions indicated the overall psychological distress level of the respondents.

2.2.4 Accident proneness questionnaire

Gauchard et al. [33] measured accident proneness of 1305 railway workers with an injury frequency questionnaire. The current study employed a similar method. The accident proneness questionnaire was used to measure the injury frequency of the participants in previous three working years. The participants needed to answer the question on “injury frequency in previous three working years” with numbers.

2.3 Procedure

The participants signed the informed consent before the study commenced. The survey was conducted in a fixed sequence for all the participants: MSDs Likert scale, Eysenck Personality questionnaire, SCL-90 scale, and accident proneness questionnaire. First, the participants were surveyed with the MSDs Likert scale. Second, the participants were surveyed with Eysenck Personality questionnaire. Third, the participants were surveyed with the SCL-90 scale. In the end, the participants were surveyed with an accident proneness questionnaire, and they needed to report their “injury frequency in previous three working years” with numbers. After the survey, the scores of each sample were calculated. Only neuroticism dimension of Eysenck Personality was analyzed in this study, because some existed studies [19, 20] reported that the neuroticism dimension of Eysenck personality was one of the high risk factors of accident proneness.

2.4 Data analysis

The surveyed data was analyzed by SPSS 17. The means of the extent of MSDs on the body parts of the coal miners were compared. Then nine dimensions of psychological distress of the coal miners were compared with Chinese norms. Analysis of Variance (ANOVA) was used to determine whether there were any significant differences in MSDs, neuroticism dimension, psychological distress and accident proneness of the coal miners from different working age. ANOVA was also used to analyze whether there were any significant differences in those of the coal miners from different work type groups. Correlation Analysis was used to analyze the correlations among working age, MSDs, neuroticism dimension, psychological distress, and accident proneness.

3 Results

3.1 Comparison of the extent of MSDs on the body parts of the coal miners

Figure 1 indicates the comparison of the means of the extent of the MSDs of the coal miners. The waist had the highest reported MSDs, followed by knees, legs, neck, feet (ankles), shoulders, back, and hands (wrists).

Fig.1

Comparisons of means of the extent of MSDs on the body parts.

3.2 Comparison of nine dimensions of psychological distress between coal miners and Chinese norms

In Table 1, the means of nine dimensions of psychological distress of coal miners were higher than those for the Chinese norms [36], except for interpersonal sensitivity dimension. The coal miners had higher somatization, obsessive-compulsive, depression, anxiety, hostility, phobic anxiety, paranoid ideation, and psychoticism scores than ordinary people.

Table 1
Comparison of nine dimensions of psychological distress between coal miners and Chinese norms

SCL-90 dimension Means of samples Means of Chinese

(n = 992) normal model

(n = 724)

Somatization 1.99±0.80 1.38±0.49

Obsessive-compulsive 1.80±0.60 1.66±0.61

Interpersonal sensitivity 1.58±0.62 1.66±0.64

Depression 1.64±0.65 1.51±0.60

Anxiety 1.82±1.34 1.41±0.44

Hostility 1.59±0.68 1.48±0.56

Phobic anxiety 1.40±0.61 1.23±0.37

Paranoid ideation 1.52±0.60 1.46±0.59

Psychoticism 1.55±0.61 1.32±0.44

SCL-90 dimension	Means of samples	Means of Chinese
Somatization	1.99±0.80	1.38±0.49
Obsessive-compulsive	1.80±0.60	1.66±0.61
Interpersonal sensitivity	1.58±0.62	1.66±0.64
Depression	1.64±0.65	1.51±0.60
Anxiety	1.82±1.34	1.41±0.44
Hostility	1.59±0.68	1.48±0.56
Phobic anxiety	1.40±0.61	1.23±0.37
Paranoid ideation	1.52±0.60	1.46±0.59
Psychoticism	1.55±0.61	1.32±0.44

3.3 Comparison of MSDs, neuroticism dimension, psychological distress and accident proneness of the coal miners from different working age

The surveyed data were analyzed using Analysis of Variance (ANOVA) with SPSS17. MSDs refer to the MSDs of whole body, which was the sum of the MSDs of all eight body parts. The comparison of the means and standard deviations of MSDs, neuroticism, psychological distress and accident proneness of the coal miners from groups with different working years are shown in Table 2. The data indicated significant difference (p = 0.003) in the MSDs of coal miners from groups with different working age, such that the extent of the MSDs of the coal miners intensified with the increasing working age. There was a significant difference (p = 0.000) in the psychological distress of the coal miners from groups with different working age, such that the psychological distress of the coal miners intensified with the increase of working age. No significant difference in neuroticism dimension and accident proneness of the coal miners between groups with different working age was found.

Table 2
Means and standard deviations (in brackets) of MSDs, neuroticism dimension, psychological distress, and accident proneness of the coal miners from groups with different working age (n = 992)

Working age group MSDs Neuroticism Psychological Accident

dimension distress proneness

1 17.68 8.86 138.07 1.48

Working age≤5 (7.426) (6.270) (47.458) (0.800)

(n = 196)

2 18.97 9.35 144.26 1.59

5<Working age≤10 (7.294) (6.237) (48.316) (0.818)

(n = 282)

3 19.70 9.93 156.00 1.63

10<Working age≤20 (7.737) (6.219) (56.990) (0.810)

(n = 252)

4 20.29 10.17 159.55 1.63

Working age>20 (8.254) (6.749) (59.995) (0.877)

(N = 262)

p 0.003^* 0.121 0.000^* 0.213

Working age group	MSDs	Neuroticism	Psychological	Accident
1	17.68	8.86	138.07	1.48
Working age≤5	(7.426)	(6.270)	(47.458)	(0.800)
(n = 196)
2	18.97	9.35	144.26	1.59
5<Working age≤10	(7.294)	(6.237)	(48.316)	(0.818)
(n = 282)
3	19.70	9.93	156.00	1.63
10<Working age≤20	(7.737)	(6.219)	(56.990)	(0.810)
(n = 252)
4	20.29	10.17	159.55	1.63
Working age>20	(8.254)	(6.749)	(59.995)	(0.877)
(N = 262)
p	0.003^***	0.121	0.000^***	0.213

Note: n = 992; ^***p < 0.005.

3.4 Comparison of MSDs, neuroticism dimension, psychological distress, and accident proneness of coal miners from different work type groups

Table 3 shows the comparison of MSDs, neuroticism dimension, psychological distress, and accident proneness of the coal miners from nine different work type groups. Significant differences (p = 0.001) were found in MSDs of the coal miners from different work type groups. The type of work that recorded the most serious MSDs was blasting, followed by coal mining, blasting assistant, other work types, gas detection, carriage, digging, electromechanical work, water detection.

Table 3
ANOVA results of MSDs, neuroticism dimension, psychological distress and accident proneness in coal miners from different work types

Type of work MSDs Neuroticism Psychological Accident

dimension distress proneness

Water detection 14.44±5.388 7.11±7.474 139.94±37.106 2.00±1.323

Electromechanical work 17.11±6.358 10.12±6.580 146.46±50.150 1.55±0.872

Digging 18.88±7.389 9.07±6.179 142.74±57.851 1.58±0.827

Carriage 18.98±7.361 9.94±6.546 143.26±44.710 1.51±0.767

Gas detection 19.22±7.541 10.61±6.285 156.60±51.841 1.72±0.893

Other work types 19.53±7.524 10.44±6.159 157.35±57.062 1.66±0.926

Blasting assistant 20.52±8.454 10.33±5.285 163.94±45.589 2.00±1.000

Coal mining 20.57±8.674 8.91±6.508 154.54±58.941 1.58±0.757

Blasting 23.00±8.373 10.37±5.283 182.13±55.611 1.53±0.697

p 0.001^*** 0.186 0.010^* 0.144

Type of work	MSDs	Neuroticism	Psychological	Accident
Water detection	14.44±5.388	7.11±7.474	139.94±37.106	2.00±1.323
Electromechanical work	17.11±6.358	10.12±6.580	146.46±50.150	1.55±0.872
Digging	18.88±7.389	9.07±6.179	142.74±57.851	1.58±0.827
Carriage	18.98±7.361	9.94±6.546	143.26±44.710	1.51±0.767
Gas detection	19.22±7.541	10.61±6.285	156.60±51.841	1.72±0.893
Other work types	19.53±7.524	10.44±6.159	157.35±57.062	1.66±0.926
Blasting assistant	20.52±8.454	10.33±5.285	163.94±45.589	2.00±1.000
Coal mining	20.57±8.674	8.91±6.508	154.54±58.941	1.58±0.757
Blasting	23.00±8.373	10.37±5.283	182.13±55.611	1.53±0.697
p	0.001^***	0.186	0.010^*	0.144

Note: n = 992; ^* p < 0.05; ^*** p < 0.005.

The results of the Least Significant Difference (LSD) test indicated that blasting had significant differences in MSDs among the work types, including digging (p = 0.026), carriage (p = 0.030), water detection (p = 0.006) and electromechanical work (p = 0.002). Coal mining had significant differences with digging (p = 0.020), carriage (p = 0.033), water detection (p = 0.019) and electromechanical work (p = 0.000). Electromechanical work had significant differences with digging (p = 0.037), gas detection (p = 0.048), carriage (p = 0.032) and other work types (p = 0.020).

Significant differences (p = 0.010) were also found in the psychological distress of the coal miners from different work type groups. The type of work having the most serious psychological distress was blasting, followed by blasting assistant, other work types, gas detection, coal mining, electromechanical work, carriage, digging, and water detection.

The results of the LSD post hoc test indicated that blasting had significant differences in psychological distress among the work types, including digging (p = 0.002), carriage (p = 0.003), coal mining (p = 0.032), and electromechanical work (p = 0.007). Digging had significant differences with coal mining (p = 0.021) and other work types (p = 0.034), while carriage had significant differences with coal mining (p = 0.032) and other work types (p = 0.044).

There was no significant difference in neuroticism dimension and accident proneness of the coal miners between different work types group.

3.5 Correlation among working age, MSDs, neuroticism dimension, psychological distress, and accident proneness

Table 4 shows the correlation among working age, MSDs, neuroticism dimension, psychological distress, and accident proneness. All correlations between factors were statistically significant except of the correlation between working age and accident proneness.

Table 4
Correlations among the data of working age, MSDs, neuroticism dimension, psychological distress and accident proneness

Factor Working years MSDs Neuroticism dimension Psychological distress Accident proneness

Working age 1

MSDs 0.117^** 1

Neuroticism dimension 0.076^* 0.263^ 1

Psychological distress 0.152^ 0.514^ 0.435^ 1

Accident proneness 0.059 0.127^ 0.088^ 0.174^** 1

Factor	Working years	MSDs	Neuroticism dimension	Psychological distress	Accident proneness
Working age	1
MSDs	0.117^**	1
Neuroticism dimension	0.076^*	0.263^**	1
Psychological distress	0.152^**	0.514^**	0.435^**	1
Accident proneness	0.059	0.127^**	0.088^**	0.174^**	1

Note: Values are Pearson’s correlations; n = 992; ^* p < 0.05; ^** p < 0.01.

Table 5 shows the correlation between the data of MSDs of eight different body parts and accident proneness. All correlations between factors were statistically significant except of the correlation between MSDs of shoulders and accident proneness.

Table 5

Correlation between the data of MSDs of eight different body parts and accident proneness

Factor	MSDs of	MSDs of	MSDs of	MSDs of	MSDs of	MSDs of	MSDs of	MSDs of
	neck	shoulders	back	waist	hands (wrists)	legs	knees	feet (ankles)
Accident proneness	0.101^**	0.058	0.137^**	0.086^**	0.081^*	0.130^**	0.065^*	0.103^**

Note: Values are Pearson’s correlations; n = 992; ^*p < 0.05; ^**p < 0.01.

Table 6 shows the correlations between data of nine dimensions of psychological distress and accident proneness. Accident proneness correlated significantly with all factors.

Table 6

Correlation between the nine dimensions of psychological distress and accident proneness

Factor	Somatization	Obsessive-	Interpersonal	Depression	Anxiety	Hostility	Phobic	Paranoid	Psychoti-
		compulsive	sensitivity				anxiety	ideation	cism
Accident proneness	0.132^**	0.124^**	0.143^**	0.179^**	0.073^*	0.187^**	0.223^**	0.198^**	0.191^**

Note: Values are Pearson’s correlations; n = 992; ^*p < 0.05; ^**p < 0.01.

4 Discussion

4.1 MSDs, neuroticism dimension, psychological distress, and accident proneness of Chinese coal miners

From the data analysis, Chinese coal miners had more serious MSDs of waist, knee, legs, and neck. The result was different from that of Torma-Krajewski et al. [4] who reported that US miners had more serious MSDs in their back, hands (wrists), shoulders, and neck. The reason for the discrepancy might be the difference in the working condition of mines in China and in US. In coal mines more automatic machines and advanced technologies have been used in US than in China, so there is fewer manual handling work for miners to do in US than in China.

The psychological distress dimensions of coal miners were higher than those of Chinese norms, except for interpersonal sensitivity dimension. Chinese coal miners had higher levels of somatization, obsessive-compulsive, depression, anxiety, hostility, phobic anxiety, paranoid ideation, and psychoticism than ordinary people because of the special work and their working conditions. The results indicated that coal miners had mental health problems, which were serious and worrisome. The result was similar with that reported by Song et al. [8].

As working age increased, the extent of the MSDs of the coal miners intensified. This could be attributed to the heavy work and long working age, which affected the physiological health of the miners and led to their MSDs. Other works also reported that the physiological function of older miners declined [17]. In the current study, the physiological state of the coal miners was bad, especially those with longer working age. With increasing working age, the psychological distress of the coal miners also intensified. Those findings illustrated that longer working age resulted in the increasing seriousness of psychological problems developed by the coal miners.

Meanwhile, there was a significant difference in MSDs acquired by coal miners between different work type groups. The types of work with the highest and lowest MSDs were blasting and water detection respectively. This could be attributed to the different types of work or work tasks that affected the MSDs of the miners.

Significant differences were also recorded in the psychological distress of the coal miners between different work type groups. The types of work with the highest and lowest levels of psychological distress were blasting and water detection respectively. It can be assumed that the differences in the various work types groups affected the psychological state of the miners.

Neuroticism positively correlated with accident proneness. This finding was similar with the conclusion of Li et al. [20], who reported that the neuroticism dimension of pilots helped predict the accident proneness of the pilots. In future studies, the neuroticism measurement may be used as a tool in predicting levels of accident proneness in other professions.

MSDs positively correlated with accident proneness. This can be explained by the fact that coal miners are exposed to various risks in their workplaces. Coal miners with higher MSDs were also found to be easily injured in the work. Therefore, coal miners must have strong skills in terms of concentration, estimation and reaction to detect and avoid danger, as well as to reduce and prevent injuries [19]. MSDs of the back, legs, and feet (ankles) were the three most important indicators that had significantly positive relationships with accident proneness. Thus, the MSDs of these three body parts should be given close attention.

Furthermore, coal miners with more serious psychological distress had higher accident proneness. Phobic anxiety, paranoid ideation, and psychoticism dimension of psychological distress were the three most important indicators that had significantly positive relationships with accident proneness. The result was different from that of Chu et al. [19] who reported that the depression dimension of psychological distress was one of the high risk factors of work injury.

4.2 Implications

The discussion illustrated that accident proneness of coal miners was related to neuroticism, psychological distress, and MSDs of the coal miners. The safety of every coal miner was the premise of the safety of the whole coal mine. Therefore, the physiological and psychological health of the coal miners should be seriously considered in coal mine safety management. Based on the findings, some implications are provided for coal mine safety management in China.

First, psychological measurements should be employed in the selection of coal miners for employment. The psychological health status of applicants can be determined using psychological measurements (e.g., Eysenck Personality questionnaire, SCL-90 scale). With this, the applicants with higher neuroticism or psychological illness can be timely and accurately identified and will not be selected. Thus, possible accidents due to individual psychological reasons can be reduced or prevented. The neuroticism dimension and psychological distress of coal miners should be given more attention. The Eysenck Personality test has been considered in pilot selection [20], and can be applied in the coal mine industry.

Second, regular physical examination of MSDs is needed for the coal miners. If the miners are found to have MSDs during the examination, then treatment and protection (e.g. using protective equipments and recovery methods) should be implemented immediately. In this case, the medical professionals should focus more on the waist, knees, legs, and neck.

Third, coal miners with longer working age should take more safety precautions at work. While coal miners with longer working age have good working experience and have more important roles, the decline in their physiological functions should be considered [17]. This is because such coal miners are more easily injured in the complicated and dangerous environment of the coal mines they work in. Thus, more frequent MSDs examinations and psychological measurements, including Eysenck personality and psychological distress test, should be conducted for coal miners with longer working age.

4.3 Limitations

The present study has some limitations. Firstly, this study surveyed MSDs, personality traits, psychological distress and accident proneness of coal miners from only one coal mine in China and they may not be representative of Chinese coal miners nationwide. More representative results would have been obtained by conducting studies in more coal mines. Secondly, it used MSDs Likert scale to measure MSDs of miners, which was reported by miners themselves, so this method is less objective. In future research, more objective methods should be applied to measure MSDs.

5 Conclusions

Existing studies on the physiological and psychological states of the coal miners can be found, but no study has focused on the relationships between the physiological and psychological states and work safety of coal miners. This research is the first to analyze the relationship among the physiological and psychological states as well as the accident proneness of coal miners. The results of this study are expected to arouse the awareness of proper coal mine management. Specifically, identifying MSDs, neuroticism, and psychological distress of the coal miners is important to work safety and should be given more attention by the management. The findings of this study can serve as a helpful reference for the government and related organizations when formulating policies and strategies to help the coal mines improve safety management. Different high-risk industries can also learn from one another in the area of safety management [37]. In relation to this, the present study can provide insights into other high-risk industries, e.g., chemical and power industries.

Conflict of interest

There are no competing interests.

Footnotes

Acknowledgments

This work is supported by grants from the National Natural Science Foundation of China (71471144, 71071126, U1261111).

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