Sociodemographic and Workplace Determinants of Quality of Life (Qol) Among Quarry Workers in Nigeria: A Cross Sectional Study

Abstract

By engaging in one form of works, people earn means of support, establish their identities, perceive life as meaningful and establish social network with others. The quality of life is a product of several factors inherent in the work environment. Quality of life is an important topic in every organization as it determines the future of the organization. This study assessed the sociodemographic and workplace determinants of Quality of life (Qol) among quarry workers in Nigeria. A cross-sectional study was conducted among the respondents. Data were collected using self-administered questionnaires consisting of socio-demographic characteristics, Standard Nordic Musculoskeletal Questionnaire and World Health Organization Quality of life BREF questionnaires (WHOQOL-BREF). A total of 266 quarry workers involved in this study were selected through systematic random sampling technique. The data were analyzed using SPSS version 26. Simple and multiple logistic regression were used to identify the determinants of quality of life among the respondents. The result showed that majority of the respondents (74.1%) had poor quality of life with variation across four domains of physical, environmental, socio relationship and psychological. Following multiple logistic regression modeling, WRMSDs (ORadj 4.24, 95% CI [1.84, 9.77], p-value = .001) and the poor work design (ORadj 3.22, 95% CI [1.52, 6.82], p-value = .002) remained significant determinants of Qol. This study showed poor quality of life among quarry workers in Ebonyi state, Nigeria. Those with WRMSDs and had poor work design were more likely to have poor quality of life compared to those who had no WRMSDs and work in well-designed workplace.

Plain Language Summary

Purpose: This study sought to determine the sociodemographic and workplace factors associated with quality of life among quarry workers. Method: A cross-sectional study was conducted among the respondents using self-administered questionnaires consisting of socio-demographic characteristics, Standard Nordic Musculoskeletal Questionnaire and World Health Organization Quality of life BREF questionnaires (WHOQOL-BREF). A total of 266 quarry workers involved in this study were selected through systematic random sampling technique. The data were analyzed using SPSS version 26. Simple and multiple logistic regression were used to identify the determinants of quality of life among the respondents. Result The result showed that majority of the respondents (74.1%) had poor quality of life with variation across four domains of physical, environmental, socio relationship and psychological. Following multiple logistic regression modeling, WRMSDs (ORadj 4.24, 95% CI [1.84, 9.77], p-value = .001) and the poor work design (ORadj 3.22, 95% CI [1.52, 6.82], p-value = .002) remained significant determinants of Qol. Conclusion: This study showed poor quality of life among quarry workers in Ebonyi state, Nigeria. Those with WRMSDs and had poor work design were more likely to have poor quality of life compared to those who had no WRMSDs and work in well-designed workplace. Implications: there is need to implement measures to ensure safety of workers at work places and to ensure satisfaction with works and working condition as this impacts greatly on their overall quality of life. Limitation: this finding is specific to quarry workers. Therefore over generalization to other company workers may not be appropriate.

Keywords

Background

Working is regarded as one of the major things that guide individual life and, because of the vitality of work in the daily life of individuals; work must be understood across economic, cultural and social spheres. By engaging in one form of works, people earn means of support, establish their identities, perceive life as meaningful and establish social network with others (Shea-Van Fossen & Vredenburgh, 2014). Despite the fact that work is vital for life and health, the organizational pattern, mode of operation and the related factors have reportedly caused numerous diseases among workers (De Sio et al., 2017; Shea-Van Fossen & Vredenburgh, 2014). Psychosocial factors arising from the interaction between work description, content, work organization, working condition, level of technological advancement and the workers’ level of competence, needs, resources and other personal factors all exert untold effects on the workers’ wellbeing and quality of life (Greco, 2014; Stavroula & Aditya, 2013).

Quality of Life according to World Health Organization (WHO) is seen as an “individual’s perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns” (Srivastava & Kanpur, 2014; Vahedi, 2010). This therefore involves four main areas of work life: safety at work; employee health care; adequate working time; and a commensurate salary (Mazlan et al., 2018; Pandey & Tripathi, 2018). Individual worker perceives high quality of life when he has positive feelings toward his work and its prospect, and hence motivated to stay on the job and commit to its performance (Shariat et al., 2015).

The quality of life is a product of several factors inherent in the work environment (De Sio et al., 2017). The performance of the employees at various work positions is intricately related to the set of factors affecting workers’ health, well-being, habits, work environment and quality of life. Moreover, quality of life is also reported to be significantly associated with employees’ job satisfaction, high moral, output at work, level of wellness, job security and safety at work (Waghmare & Dhole, 2017) and varies across occupation, occupational disorders and socio-demographic characteristics (Haukenes et al., 2014; Kim et al., 2015; Mathew et al., 2016; Mazlan et al., 2018; Veeri et al., 2019; Yang et al., 2019).

Quarry industries are one of the important industries across the world serving as major sources of raw materials for key developmental strides in the society (Aq et al., 2011). According to British Geological Survey (2017) quarry work is second to agriculture as the major sources of employment mostly in rural areas with the products exported to the developed areas as finished products. It accounts for over 40 million Euros economic growth across countries (Wolszczak-Derlacz & Parteka, 2018). Over 20 million workers earn living from quarry industries in developing countries (Adeyemi et al., 2013; Uwakwe et al., 2015).

Quarry industries in Nigeria have been recognized as one of the major sources of employment and livelihood among Nigerians in both rural and urban areas besides the main occupation such as civil service and farming. At least every state of the federation has one or two quarry sites where stones are crushed for many purposes and employs reasonable number of workers in different capacity such as drivers, blasters and lifter, most of which are literate and few illiterate men and women. Quarrying work is often characterized by very risky and challenging work conditions that involve manual handling of material, lifting of heavy objects, movements and tasks that are recurrent, manual exertion of forces and possible exposure to segmental or whole-body vibration. In Nigeria automation is often substituted with human efforts thereby expose to health risk and likelihood of poor quality of health. This is due to the impact of the tool handling that predisposes workers to WRMSDs.

Despite the above enormous contributions, quarry works have been characterized by poor technological involvement and use of human being in place of automation (Ayub & Shah, 2018; Wolszczak-Derlacz & Parteka, 2018). Poor condition of work, lack of job security and low monthly earning were also common among quarry workers in developing countries where quarry industries are mostly located for want of low cost for labor (Wolszczak-Derlacz & Parteka, 2018). It has also been reported as the riskiest job across occupations with various occupational disorders (Azlina et al., 2014; Egwuonwu & Abidemi, 2013; Health and Safety Executive, 2018). Assessment of quality of life is a vital managerial tool to map out preventive measures for health deviations as a result of work activities and formulation of policies for safety at work (Cacciari et al., 2017). This study therefore assessed the quality of life and the associated factors among quarry workers in Nigeria.

Materials and Method

Study Design

This study utilized cross-sectional survey design to assess Sociodemographic and workplace determinants of Quality of life (Qol) among the quarry workers in Nigeria.

Area of the Study

This study was carried out in the quarry industry located at Umuoghara in Ezza North Local Government Area of Ebonyi State, Nigeria. There are about 50 plants in the company all located within one premise. There are about 800 workers all together in the company according to the register from the administrative officer. These workers are usually recruited by the companies and required to break stone lumps into useable sizes for construction works using human efforts. In the simplest form, human efforts are used to substitute automation in quarry industry. Certain tools like Monday hammers are among the common tools used to crack the stones while transportation to place of marketing is done using human heads.

Inclusion Criteria

Male and female workers working in the quarry industries in Ebonyi state at the time of this study. Workers involved in the stone processing such as the blasters, operators of crushers, lorry drivers, manual stone handlers and loaders. Workers who have worked for at least 1 year in the quarry company were considered for inclusion.

Ethical Review

The ethical approval for this study was granted by Human Research Ethical Committee of Universiti of Sains Malaysia (USM/JEPeM/20020133) Ethical committee and letter of permission from the chairman of the quarry company. Approval Number: HHHH/JEPeM/2002HHHH

Sample Size Estimation

The sample size for this study was determined according to the objectives of the study. Objective 1: to determine the quality of life among the quarry workers. Sample size was determined using single proportion formula as follows: N = p (1−p)(Z/E)²N = sample size, p = proportion of “very poor quality of life” in population, Z = standard normal distribution, E = precision, Where p = .5(Mazlan et al., 2018), Z = 1.96 and E = 0.06. n = 0.5(1–0.5)(1.96/0.06)^2.

Therefore n=0.25×32.67 × 32.67. N = 266 respondents.

Objective 2: to determine the sociodemographic and workplace determinants of quality of life among quarry workers. The sample size was calculated using PS software. Alpha = 0.05, power = 0.8, m = 1, P0 = 0.50 (Mohammad et al., 2018), P1 = 0.75 (expert opinion), sample size=174 (Dissatisfied/poor QOL=116, Satisfied/good QOL=58). P0=probability of exposure to the risk (WRMSDs) in controls (good QOL) OR WRMSDs/quarry workers. P1 = probability of exposure to the risk (WRMSDs) in cases (poor QOL).

Therefore, the largest sample size for this study was 266 respondents as shown in objective 1 above.

Instrument for Data Collection

The questionnaires used for this study had three parts: part A, B, and C. Part A consist of the socio-demographic variables of the respondents and the workplace factors while part B was World Health Organization Quality of Life questionnaire (WHOQOL BREF) with 26 items and Part C was the Standard Nordic Musculoskeletal questionnaire used to assess the rate of work related musculoskeletal disorders. The Nordic Musculoskeletal question was scored 1 for yes and 0 for no. It is used to assess the disorders of the musculoskeletal system within 12 months duration. It has questions on the nine parts of the body that are commonly affected by WRMSDs. This questionnaire has been tested for reliability by the developers through test retest method with a good Cronbach’s alpha value of .78, .91, and 1.00 across the times. The WHOQOL questionnaire was developed by WHO in 1995 to assess the effect of workers’ work on their quality of life and impacts of disease on the workers life. It was developed based on the perception of the individual about their quality of life in different domains. It was designed for transcultural uses and in clinical and research studies.

The quality of life questionnaire has five likert scale items that were scored using “1” = very dissatisfied, “2” = dissatisfied, “3” = neither dissatisfied nor satisfied, “4” = satisfied, “5” = very satisfied. To further determine the predictors of quality of life among the quarry workers using multiple logistic regression, the researcher had categorized the quality of life from four domains into two domains (Dissatisfied/poor Qol and satisfied/good Qol). This categorization was made based on the workers responses to one of the questions, which seeks to determine the overall level of the workers’ quality of life. This was done by grouping satisfied and very satisfied responses together as good or satisfied QOL while dissatisfied, very dissatisfied and not sure responses were grouped into poor or dissatisfied Qol. The reliability of WHOQOL questionnaire has been established with a Cronbach alpha value of .7 and .8 across studies (Cheung et al., 2017; Najafi et al., 2009).

Sampling Method and Data Collection

The respondents for this study were selected through systematic random sampling method from 800 sample frame. All the 800 workers are from the same quarry company in Umuoghara in Ezza North Local Government Area. The sampling was done during their meeting time when all the workers were gathered together. Statistical package for social sciences (SPSS) was used to randomly select number 2 which was used in the sampling. Every second name on the sample frame was selected for this study. The researcher approached the respondents through the management and introduced himself to the respondents. Then the purpose of the study was explained verbally and in written form prior to distribution of the questionnaires. He then swiftly screened for the inclusion criteria and applied systematic random sampling as described above to sample the required respondents. The respondents were requested to decide voluntarily either to participate in this study or not. Those who volunteered to participate received the respondents’ information sheet and consent forms and were asked to sign. The researcher distributed the questionnaires to the respondents and retrieved same after responses on same day. A total of 266 questionnaires were distributed and retrieved from the workers. The data collection took place from July to September 2020.

Data Analysis

Data were coded and analyzed using SPSS 26. The socio-demographic variables of the respondents were presented using descriptive statistics. Descriptive statistics (frequency, percentage and mean) was used to determine the level of quality of life of the quarry workers. The score on the quality of life were computed and converted to scale 100 as per scoring guideline. Simple logistic regression was used to screen the independent variables. The independent variables with significance value less than .25 were selected for multiple logistic regression. Backward and forward variable selection approach was used in the multiple logistic analysis. The final multiple logistic regression model included those independent variables with significant value of less than .05. Model fitness was then assessed based on Hosmer and Lemershow test, classification table and receiver operating characteristics (ROC).

Results

Table 1 shows the socio-demographic characteristics of the respondents. The mean age of the respondents was 31.0 (8.28). The majority of the workers are young, less than 30 years old (46.2%), males (66.9%) and majority had secondary level of education (49.6%). Most of the them were married (49.6%), worked as blasters (24.4%) and had normal BMI (62.9%). Majority of the respondents had 0 to 2 children (54.5%), 35.8% had monthly income of more than 10,000 Naira per month and 51.5% had 3 to 6 family members being catered for by the monthly income.

Table 1.

Socio-demographic Characteristics of the Quarry Workers (N = 266).

Variables	Mean(SD)	Frequency	Percentages(%)
Age	31.0 (8.28)
<20		17	6.4
20–29		106	39.8
30–39		92	34.6
40–49		51	19.2
Gender
Female		88	33.1
Male		178	66.9
Education
No formaleducation		0	0
Primary education		94	35.5
Secondary education		132	49.6
Tertiary education		40	15.1
Marital status
Single		106	39.8
Married		132	49.6
Divorced		14	5.3
Widow/widower		14	5.3
Work type
Blaster		65	24.4
Drivers		38	14.3
Loaders		56	21.1
Manual workers		41	15.4
Evacuators		28	10.5
Operators		38	14.3
Health relatedfactors
BMI	26.2 (4.10)
Obese (BMI ≥ 30)		98	36.8
Not obese		167	62.8
Other health problem
No problem		248	93.2
Has problem		18	6.8
Use of medication
Yes		58	21.8
No		208	78.2
Smoke
Yes		108	40.5
No		158	59.5
No. of children	2.3 (2.34)
0–2		145	54.5
03-May		87	32.7
06-Aug		34	12.8
Monthly income(Naira)	10,625#(7,500)
<10,000		160	60.2
10,000–15,000		54	20.3
16,000–21,000		25	9.4
22,000–27,000		16	6
28,000–33,000		8	3
34,000–39,000		1	0.4
≥40,000		2	0.8
No. of family members
<3		78	29.3
03-Jun		137	51.5
07-Oct		50	18.8
Nov-14		1	0.4

Table 2 shows the work-related factors of the quarry workers. About 66.2% of the respondents had work experience of 1 to 5 years with mean 4.9 years. Majority of the respondents (96.6%) worked for 7 to 9 hr per day (mean 8.0 hr), mean break time per day 14.0 min. About 86.6% reported lack of work training while most of the respondents (94.4%) worked under high temperature. Majority of the workers (95.5%) reported working with injury while 97.4% of the respondents never used personal protective equipment (PPE) at work.

Table 2.

Work-Related Factors of the Quarry Workers (N = 266).

Variables	Mean(SD)	Frequency(n)	Percentages(%)
Work experience	4.9 (3.00)
01-May		176	66.2
06-Oct		76	28.6
Nov-15		11	4.1
16–20		3	1.1
Working hours	8.0 (4.3)
<4		1	0.4
04-Jun		5	1.9
07-Sep		257	96.6
10-Dec		3	1.1
Duration of break time	14.0 (11.80)
<10 min		100	37.6
10–15 min		25	9.4
20–25 min		89	33.5
30–35 min		50	18.8
>35 min		3	0.8
Exposure to vibration
Yes		124	46.6
No		142	53.4
Work training
No		231	86.8
Yes		35	13.2
High temperature workplace
Yes		251	94.4
No		15	5.6
Repetitive work
Yes		264	99.2
No		2	0.8
Awkward posture
Yes		235	88.3
No		31	11.7
High job demands
Yes		262	98.5
No		4	1.5
Working with injury
Yes		254	95.5
No		12	4.5
Use of PPE
Yes		7	2.6
No		259	97.4
Poor work design
Yes		231	87.2
No		34	12.8

Table 3 shows the prevalence of WRMSDs among quarry workers. More than half of the respondents (89.8%) reported having WRMSDs. Majority of the respondents (45.5%) reported experience of moderate pains followed by the mild level of pain (24.8%). About 62.4% of the respondents had difficulties in performing daily activities due to the WRMSDs.

Table 3.

Prevalence of Work-Related Musculoskeletal Disorders (N = 266).

Items	Frequency	Percentage (%)
WRMSDs
Yes	239	89.8
No	27	10.2
Level of pain
No pain	26	9.8
Mild pain	66	24.8
Moderate pain	121	45.5
Severe pain	53	19.9
Very severe pain	0	0
Effect of pain on activities
Yes	166	62.4
No	100	37.6

Note. WRMSDs = work-related musculoskeletal disorders. One of those without WRMSDs reported presence of pain at level of pain hence No pain = 26.

In terms of quality of life, about 74.1% of the respondents reported poor overall quality of life with variation across domains. The physical domain had the highest mean score (62.8) closely followed by the environmental domain while the social relationship and psychological domains recorded the least mean score Table 4.

Table 4.

Domains of Quality of Life Among Quarry Workers.

Variables	Frequency	Percentage
Overall quality of life
Good Qol.	69	25.9%
Poor Qol	197	74.1%
Domains	Mean	Standard deviation
Physical domain	62.8	11.99
Psychological domain	36.7	7.573
Environmental domain	57.6	14.816
Social domain	29.8	7.146

From the simple logistic analysis number of children (p-value = .102), smoking (p-value = .157), break time (p-value = .096), type of work (Table 5; driver: p-value = .011, operator: p-value = .076), level of pain (p-value = .001), WRMSDs (p-value < .001), Age (p-value = .018), monthly income (p-value = .046), working with injury (p-value = .015), working under high temperature (p-value = .018), vibration exposure (p-value = .014) and poorly designed work (p-value = .002) showed significant association with quality of life at p-value < .05.

Table 5.

Univariant Logistic Analysis of Factors Associated With Quality of Life Among Quarry Workers.

Variable	Crude OR (95% CI)	p-Value
WRMSDs
No	1
Yes	4.28 [1.89, 9.70]	<.001*
Age	1.50 [1.07, 2.09]	.018*
Gender
Female	1
Male	1.21 [0.68, 2.15]	.519
Education
Primary	1
Secondary	1.20 [0.65, 2.18]	.562
Tertiary	1.01 [0.44, 2.31]	.985
Work experience	1.46 [(0.89, 2.37]	.134
Work type
Blaster	1
Drivers	0.31[0.13, 0.77]	.011*
Loaders	0.68[ 0.28, 1.62]	.384
Evacuators	0.68[ 0.24, 1.96]	.475
Manual work	1.10 [0.39, 3.07]	.856
Operators	0.44 [0.17, 1.09]	.076*
Marital status
Single	1
Married	1.41 [0.80, 2.50]	.237
Divorced	5.88 [0.74, 46.81]	.094*
Widow/widower	1.66 [0.434, 6.34]	.406
No. of children	1.11 [0.98, 1.25]	.102*
Income	1.00 [1.00, 1.00]^#	.046*
Family size	1.00 [0.99, 1.00]	.766
BMI	1.26 [0.77, 2.19]	.417
Smoking
No	1
Yes	0.67 [0.39, 1.17]	.157*
Health problems
No	1
Yes	1.81 [0.51, 6.46]	.359
Use of medication
No	1
Yes	0.90 [0.47, 1.73]	.746
Pain level
No pain	1
Pain	3.92 [1.72, 8.98]	.001*
Working hours	1.10 [0.80, 1.50]	.563
Break time	0.98 [0.96, 1.00]	.096*
Work with injury
No	1
Yes	4.33 [1.33, 14.15]	.015*
Work under high temperature
No	1
Yes	3.56 [1.24, 10.22]	.018*
Poor work design	1
No	3.25 [1.57, 6.76]
Yes	1	.002*
Exposure to vibration	0.50 [0.29, 0.87]
No		.014*
Yes

Significant at p-value < .25, OR = odd ratio, CI = confidence interval, BMI = Body Mass Index #0.999965 (0.999931, 0.999999).

Table 6.

Final Model of Factors Associated With Quality of Life Among Quarry Workers.

Variables	Adjusted OR (95% CI)	p-Value
Poor work design
No (R)	1
Yes	3.22 [1.52, 6.82]	.002*
WRMSDs
No (R)	1
Yes	4.24 [1.84, 9.77]	.001*

Note.*Significant at p-value < .05, B regression coefficient, CI = confidence interval.

Following the multiple logistic regression modeling, WRMSDs (p-value = .001) and the poor work design (p-value = .002) remained significantly associated with the quality of life among quarry workers in Ebonyi state Nigeria. Based on the odd ratio, those who had WRMSDs had 4 times more odds to have poor QoL compared to those who had no WRMSDs. In addition, those who had poor work design were 3 times more likely to have poor QoL.

Discussion

This study revealed that 74.1% of the quarry workers had poor quality of life. This finding could entail being dissatisfied with their work and working condition which directly affects the morale of the workers (Bhola, 2015). The quality of life of the workers is a vital element in the organizational management and plays key role in the growth and continued productivity of the organization. Therefore, deliberate efforts must be put in place to improve and maintain satisfied quality of work life among the workers as to ensure commitment and motivation on the part of the workers (Srivastava & Kanpur, 2014). The condition of work, policy on welfare of the workers and every other organizational factor capable of impacting on the workers wellbeing should be addressed among the quarry industries in a timely manner (Child & Icn, 2017). One of such key approaches includes adequate social support, proper working conditions, good remuneration and psychological supports (Tesla, 2018). Low quality of life was also indicated in other studies conducted among different group of workers (Asante et al., 2019; Cacciari et al., 2017; Szemik et al., 2019; Tesla, 2018).

The result was contrary to the study done on accredited social health activist in Malaysia which revealed that 60% of the participants reported good quality of life (Stavroula & Aditya, 2013). This difference may be attributed to condition of work at the various industries, technological involvement, level of risk, health status of the workers, socio-cultural context (Tian, 2020) and the availability of psychosocial supports across countries (Szemik et al., 2019). Also, the relevance of the needs of the individual workers have been reported to vary across culture and organizations hence may account for the differences above (Diogo et al., 2014).

Regarding the domains of the quality of life, the physical and environmental domain had the highest scores which however remained low on the 100% scale. These low scores may be accounted for by the poor working conditions and health status (Szemik et al., 2019).

More also, at univariant analysis using simple logistic regression, certain independent variables showed significant association with quality of life. The factors include age, type of work, monthly income, level of pain, working with injury and work design. This is in agreement with reports of other research studies (De Sio et al., 2017; Ganapathi, 2016; International Labour Organization, 2014; Noor & Abdullah, 2012; Szemik et al., 2019; Tian, 2020).

Moreover, the multivariable analysis of the independent variables showed that two key independent variables, WRMSDs and poor work design remained significantly associated with quality of life of the respondents. WRMSDs is significantly associated with quality of life of the quarry workers in this study with unadjusted odd (4.28) when those without WRMSDs was used as reference. This therefore means that workers with WRMSDS have high odd (4.28) of being dissatisfied with their quality of life compared to those without WRMSDs. A few studies had reported a link between poor quality of life with job dissatisfaction, safety at work and low morale (Leitão et al., 2019; Waghmare & Dhole, 2017). Therefore, appropriate preventive measures against WRMSDs is implicated by this finding as to improve the quality of life of the workers as well their commitment to organizational goal since evidence shows negative effects on poor quality of life on productivity at work (Diogo et al., 2014). This finding is in line with a study among primary healthcare workers which reported a significant association between health status and quality of life (Asante et al., 2019). A similar finding was also indicated in a study among industrial dwellers in Poland in which their health status had significant association with their quality of life (Szemik et al., 2019) and a study among physical therapist in Korea (Bae & Min, 2016).

Poor work design also had remained significantly associated with the quality of life of the workers. This implies that workers working in poorly designed work environment were 3 times more likely to have poor quality of life than those who work in well-designed workplace. Therefore, the ergonomic design of the working units in the quarry industries in Nigeria is a predictor of the highly dissatisfied quality of life among the quarry workers. Previous study has reported the reiteration among construction company workers of the need for improved working environment for efficiency and improved health at work (Eaves et al., 2016). This study further confirms this reiteration. Similar study in Malaysia showed that 80% of the respondents found their work environment and work design as safe and adequate which is not comparable to the poor workplace design reported in this study (Mazlan et al., 2018). International Labour Organization supported these findings that ergonomic principles of automation, substitution, and enclosure must be implemented in addition to ergonomic postural training of the workers to ensure safety at work (Ganapathi, 2016).

Study conducted in Taiwan reported that workplace design and other environmental factors are sources of psychological threat and burnout among workers; burnout has in turn been a significant predictor of poor quality of life among workers (Yang et al., 2019). This agrees with the result of the present study among the quarry workers in Nigeria. These predictors of quality of life need to be considered in developing interventional programs to address the welfare of the quarry workers in Nigeria as research evidence has attributed human performance at work to workplace factors experienced by the workers (Layer et al., 2009).

Conclusion

Poor quality of life among the quarry workers in Ebonyi state, Nigeria was indicated in this study across the four domains. Factors such as age, monthly income, number of children, work experience, working with injury, work types and poor work design were significantly associated with poor quality of life among the workers. Moreover, multiple logistic modeling also showed that WRMSDs and poor work design remained the significant predictors of poor quality of life among the respondents.

Strength and Limitation

The key strength of this study lies in the fact that it is the first empirical study assessing quality of life among quarry workers across Nigeria using a valid and reliable tool. The use of standard tool is the major strength of this study in that it avoided trial and error chance and its findings are reliable (Hamed, 2016). It is also the first empirical study that modeled the socio-demographic factors, workplace factors and WRMSDs with quality of life among the quarry workers in Nigeria.

The key limitation of this study lies in the cross-sectional design employed which may not have allowed for detailed exploration of the quality of life among the quarry workers. This study also focused on the quarry workers in Ebonyi state, Nigeria and therefore, the result may not be generalized to other quarry workers in other states in Nigeria.

Recommendation

Based on the result of this study, the researchers recommend that the quarry industries should be properly examined with working conditions in mind as to ensure improved welfare for the workers. The environmental health and occupational health officers should swing into actions to ensure compliance with standard of work design as majority of the workers reported poorly designed work environment and that was also found to be a major determinant of quality of life. Improved mechanization of some of the work processes especially those involving exposure to vibration and other high risk aspect is highly recommended.

Supplemental Material

sj-docx-1-sgo-10.1177_21582440231220169 – Supplemental material for Sociodemographic and Workplace Determinants of Quality of Life (Qol) Among Quarry Workers in Nigeria: A Cross Sectional Study

Supplemental material, sj-docx-1-sgo-10.1177_21582440231220169 for Sociodemographic and Workplace Determinants of Quality of Life (Qol) Among Quarry Workers in Nigeria: A Cross Sectional Study by Stanley Njaka, Dariah Mohd Yusoff, Yee Cheng Kueh, Siti Marwanis Anua and Edeogu Chuks Oswald in SAGE Open

Supplemental Material

sj-docx-2-sgo-10.1177_21582440231220169 – Supplemental material for Sociodemographic and Workplace Determinants of Quality of Life (Qol) Among Quarry Workers in Nigeria: A Cross Sectional Study

Supplemental material, sj-docx-2-sgo-10.1177_21582440231220169 for Sociodemographic and Workplace Determinants of Quality of Life (Qol) Among Quarry Workers in Nigeria: A Cross Sectional Study by Stanley Njaka, Dariah Mohd Yusoff, Yee Cheng Kueh, Siti Marwanis Anua and Edeogu Chuks Oswald in SAGE Open

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Stanley Njaka

Supplemental Material

Supplemental material for this article is available online.

Data Availability Statement

Data is available on request.

References

Adeyemi

B. O.

Majekodunmi

A. L.

Elijah

Oluwakemi

B. O.

John

Adigun

Oluwaseyi

A. D. A. A.

, (2013). Practice of occupational safety among artisanal miners in a rural community in Southwest Nigeria. Aceh International Journal of Science and Technology, 2(4), 2278–3687.

Aigbkhaode

A. Q.

Isah

E. C.

Isara

A. R.

(2011). Knowledge and practice of occupational safety among quarry workers in a rural community in Edo State. Journal of Community Medicine and Primary Health Care, 23, 16–24.

Asante

J. O.

M. J.

Liao

Huang

Y. X.

Hao

Y. T.

(2019). The relationship between psychosocial risk factors, burnout and quality of life among primary healthcare workers in rural Guangdong province: A cross-sectional study. BMC Health Services Research, 19(1), 447–510. https://doi.org/10.1186/s12913-019-4278-8

Ayub

Shah

Z. A.

(2018). Assessment of work related musculoskeletal disorders in manufacturing industry. Journal of Ergonomics, 08(03), 1–5.

Azlina

Rahman

Ismail

A. R.

Mansor

M. A.

(2014). Analysis of the perception of occupational accident in mining and quarry sector towards safe and healthy working environment. International Journal of Current Research and Academic Review, 1(1), 95–102.

Bae

Y. H.

Min

K. S.

(2016). Associations between work-related musculoskeletal disorders, quality of life, and workplace stress in physical therapists. Industrial Health, 54(4), 347–353.

Bhola

(2015). Impact of quality of work life (qwl) on quality of life (qol) of working women. Indian Streams Research Journal, 29(1).

British Geological Survey. (2017). Quarrying: Employment and economy. https://www.bgs.ac.uk/mendips/aggregates/economy.html.

Cacciari

Haddad

M. C. F. L.

Wolff

L. D. G.

Dalmas

J. C.

Pissinati

P. S. C.

(2017). Quality of life of workers who underwent work adjustments and adaptations in a public state university. Rev Gaúcha Enferm, 38(1), https://doi.org/10.1590/1983-1447.2017.01.60268

10.

Cheung

Y. B.

Yeo

K. K.

Chong

K. J.

Khoo

E. Y.

Wee

H. L.

(2017). Reliability and validity of the English-, Chinese- and Malay-language versions of the World Health Organization Quality of Life (WHOQOL-BREF) Questionnaire in Singapore. Annals of the Academy of Medicine, Singapore, 46(12), 461–469.

11.

Child

Icn

(2017). Modern slavery, child labour and unsafe work in Indian granite quarries What should companies do? The Dark Sites of Granite 2 the dark sites of granite Colofon. www.stopchildlabour.org

12.

De Sio

Cedrone

Sanità

Ricci

Corbosiero

Di Traglia

Greco

Stansfeld

(2017). Quality of life in workers and stress: Gender differences in exposure to psychosocial risks and perceived well-being. BioMed Research International, 2017, 7340781. https://doi.org/10.1155/2017/7340781

13.

Diogo

J. H.

Evandro

E. B.

Roberto

Luis

F. S.

Luis

A. P.

(2014). Quality of working life and productivity: An overview of the conceptual framework. International Journal of Managerial Studies and Research, 2(5), 87–89.

14.

Eaves

Gyi

D. E.

Gibb

A. G.

(2016). Building healthy construction workers: Their views on health, wellbeing and better workplace design. Applied Ergonomics, 54, 10–18.

15.

Egwuonwu

V. A.

Abidemi

T. B.

(2013). Okoye and Ezeukwu: Cross-sectional survey of work related musculoskeletal disorders prevalence and associated risk factors among quarry workers in southeastern Nigeria. Internet Journal of Epidemiology.

16.

Ganapathi

(2016). A study on quality of work life of workers in construction industry in Madurai district. Journal of Management Research and Analysis, 3(2), 63. https://doi.org/10.5958/2394-2770.2016.00010.7

17.

Greco

(2014). V Workshop. Health determinants: new paths for scientific research. Istituto Superiore di Sanita, Rome, Italy, Istisan Congressi 14/C2 edition.

18.

Hamed

(2016). Validity and reliability of the research instrument: How to test the validation of a questionnaire in a research. International Journal of Academic Research in Management, 5(3), 28–36.

19.

Haukenes

Farbu

E. H.

Riise

Tell

G. S.

(2014). Physical health-related quality of life predicts disability pension due to musculoskeletal disorders: Seven years follow-up of the Hordaland Health Study Cohort. BMC Public Health, 14(1), 167.

20.

Health and Safety Executive. (2018). Work related musculoskeletal disorders in great Britain 2018. www.hse.gov.nklstatistics.

21.

International Labour Organization. (2014). Safety and health at work: A vision for sustainable prevention. World Congress on Safety and Health at Work 2014: Global Forum for Prevention, Frankfurt, Germany. http://www.ilo.org/safework/info/publications/WCMS_301214/lang–tr/index.htm

22.

Kim

Park

Yang

Cho

Yoo

Park

Chung

Choi

E. M.

Han

Yang

(2015). The relationship between obesity and health-related quality of life of office workers. Journal of Physical Therapy Science, 27, 663–666.

23.

Layer

J. K.

Karwowski

Furr

(2009). The effect of cognitive demands and perceived quality of work life on human performance in manufacturing environments. International Journal of Industrial Ergonomics, 39(2), 413–421. https://doi.org/10.1016/j.ergon.2008.10.015

24.

Leitão

Pereira

Gonçalves . (2019). Quality of work life and organizational performance: Workers’ feelings of contributing, or not, to the organization’s productivity. International Journal of Environmental Research and Public Health, 16(20), 1–18. https://doi.org/10.3390/ijerph16203803

25.

Mathew

Shanbhag

N. R.

Goud

Subramanian

Lobo

Xavier

, et al. (2016). Quality of life and probable psychological distress among male workers at a construction site, Kolar district, Karnataka, India. Indian Journal of Occupational and Environmental Medicine, 20, 54.

26.

Mazlan

S. R.

Tamrin

S. B. M.

Guan

N. Y.

How

Rahman

R. A.

Basri

Shariat

(2018). Quality of work life among Malaysian osh personnel and general workers from different industries in Malaysia. Malaysian Journal of Medicine and Health Sciences, 14, 40–46.

27.

Mohammed

M. A.

Moles

R. J.

Hilmer

S. N.

Kouladjian O’Donnel

Chen

T. F.

(2018). Development and validation of an instrument for measuring the burden of medicine on functioning and well-being: the Medication-Related Burden Quality of Life (MRB-QoL) tool. BMJ Open, 8(1), e018880. https://doi.org/10.1136/bmjopen-2017-018880

28.

Najafi

Sheikhvatan

Montazeri

Sheikhfathollahi

(2009). Reliability of World Health Organization Quality of life Questionnaire in patients with cardiovascular disorders. Journal of Cardiovascular Medicine, 10(4), 291–297. https://doi.org/10.2459/JCM.0b013e328326291a

29.

Noor

S. M.

Abdullah

M. A.

(2012). Quality work life among factory workers in Malaysia. Procedia - Social and Behavioral Sciences, 35, 739–745. https://doi.org/10.1016/j.sbspro.2012.02.14435

30.

Pandey

M. K.

Tripathi

(2018). Examine the relationship between level of aspiration, believes in just world, psychological well-being and quality of work-life. Indian Journal of Health and Wellbeing, 9, 53–59.

31.

Shariat

Bahri Mohd Tamrin

Daneshjoo

Sadeghi

(2015). The adverse health effects of shift work in relation to risk of illness/disease: A review. Acta Medica Bulgarica, 42(1), 63–72.

32.

Shea-Van Fossen

R. J.

Vredenburgh

D. J.

(2014). Exploring differences in work’s meaning: An investigation of individual attributes associated with work orientations. Journal of Behavioral and Applied Management, 15.

33.

Srivastava

Kanpur

(2014). A study on quality of work life: Key elements & it’s implications. IOSR Journal of Business and Management, 16(3), 54–59. https://doi.org/10.9790/487x-16315459

34.

Stavroula

Aditya

(2013). Health impact of psychosocial hazards at work: An overview (Vol. 21). Publications of the World Health Organization.

35.

Szemik

Kowalska

Kulik

(2019). Quality of life and health among people living in an industrial area of Poland. International Journal of Environmental Research and Public Health, 16(7), 1221. https://doi.org/10.3390/ijerph16071221

36.

Tesla

E. J.

(2018). Work pattern and quality of life among construction workers in Kuniamuthur village in Coimbatore district. Journal of Social Work, Education, and Practice, 3(1), 6–9.

37.

Tian

(2020). QWL and related factors for migrant workers survey in Guangdong Province, China. Journal of Social Service Research, 46(2), 160–169. https://doi.org/10.1080/01488376.2018.1536629

38.

Uwakwe

K. A.

Agu

A. P.

Ogbonnaya

L. U.

Nwonwu

E. U.

Agwu

E. N.

Duru

C. B.

(2015). Perception and attitude towards work related ill- health and use of dust mask among crushers of selected quarry industry of Ebonyi State. Journal of Community Health and Primary Health care, 27(2), 93–103.

39.

Vahedi

(2010). World Health Organization quality-of-life scale (WHOQOL-BREF): Analyses of their item response theory properties based on the graded responses model. Iranian Journal of Psychiatry, 5(4), 140–153.

40.

Veeri

R. B.

Gupta

A. K.

Pal

Siddiqui

N. A.

Priya

Das

Pandey

(2019). Assessment of quality of life using WHOQOL-BREF in patients with visceral leishmaniasis. Health and Quality of Life Outcomes, 17, 53.

41.

Waghmare

Dhole

(2017). Quality of work life and influencing factors. International Journal of Advanced Research, 5, 1328–1332.

42.

Wolszczak-Derlacz

Parteka

(2018). The effects of offshoring to low-wage countries on domestic wages: A worldwide industrial analysis. Empirica, 45, 129–163. https://doi.org/10.1007/s10663-016-9352-4

43.

Yang

S. Y.

Hsu

D. J.

Yen

C. M.

Chang

J. H.

(2019). Predictive factors of life quality among packaging workers in Taiwan. Health Promotion International, 34(4), 751–759. https://doi.org/10.1093/heapro/day029

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.01 MB