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Brief Report
ARTICLE IN PRESS
doi:
10.25259/JNRP_42_2022

Psychological distress and quality of community life among migratory construction workers in India

Jindal School of Psychology and Counselling, O.P. Jindal Global University, Sonepat, Haryana, India
Department of Psychiatric Social Work, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India
Department of Psychology, School of Social Sciences/Psychology, Christ (Deemed to be University), Bengaluru, Karnataka, India
Department of Social Work, CMR University, Bengaluru, Karnataka, India
Department of Social Work, Central University of Karnataka, School of Social and Behavioral Sciences, Gulbarga, Karnataka, India
Corresponding author: Govindappa Lakshmana, Department of Social Work, Central University of Karnataka, School of Social and Behavioral Sciences, Gulbarga, Karnataka, India. lakshmanag@cuk.ac.in
Licence
This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-Share Alike 4.0 License, which allows others to remix, transform, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.

How to cite this article: Sriramalu SB, Elangovan AR, Annapally SR, Birudu R, Lakshmana G. Psychological distress and quality of community life among migratory construction workers in India. J Neurosci Rural Pract, doi: 10.25259/JNRP_42_2022

Abstract

Objectives:

The objectives of this study are to elicit sociodemographic details, assess the level of psychological distress, and measure the quality of community life (QoCL) of migratory construction workers.

Materials and Methods:

A cross-sectional research design and survey method of sampling was followed. The semi-structured interview schedule, self-reporting questionnaire, and QoCL scale were used as measures for the study.

Results:

Out of 75 respondents, 37 (49.3%) did not have formal education, 38 (50.7%) have migrated for less than a month duration, 33 (44.0%) respondents migrated with their families. The mean age of respondents was 32.03 ± 9.82 years. About 48 (64.0%) were identified as potential respondents for psychosocial care and female respondents (M = 12.90 ± 4.03, t = −3.03, P < 0.003) have higher distress than males (M = 9.50 ± 4.56, t = −3.03, P < 0.003) ones. Overall, QoCL indicated a below moderate (59.08 ± 8.31) level.

Conclusion:

The distress was high and QoCL indicated a below moderate level. Intersectoral and community mental health services were required to enhance QoCL and reduce distress among migratory construction workers.

Keywords

Migration
Workers
Construction
Distress
Quality of community life

INTRODUCTION

The movement of people from one place to another has existed throughout human civilization. The decision on whether to move, how, and where to go is complex and could be driven by several factors.[1] The National Sample Survey Organization (NSSO) estimated that 326 million of the population are migrants.[2] According to the Census of India, 31.16% of the urban population are migrants and nearly 20.5 million people migrate annually to urban areas.[3]

The NSSO reported that seasonal and short-term migrants are young and they are 15–29 years age group.[2] In the development sector, the construction industry is India’s second-largest generator of the labor force, with 40 million migrants,[4] most of whom are seasonal and short-term migrants.[5] This sector attracts and employs many, especially unskilled or semi-skilled manual laborers. However, studies have found that most short-term migrants belong to socioeconomically disadvantaged groups, with basic educational attainment, limited assets and resources, debt cycles, agricultural losses, and huge expenditures on family and social ceremonies.[5,6]

Migration interrupts social interaction with families, friends, communities, and value systems, changing behavior and adapting to a new psychosocial environment.[7] In the process of migration, women, children, and elders are more vulnerable and they require psychosocial support at either their origin or destination places.[8] Migration creates job insecurity, separation from sociogeographical connectivity, poor housing facilities, lack of recreation and health services, lack of protection and safety, and other concerns.[9,10]

Studies have found social discrimination toward migrants, lack of civic amenities at the destination places is a greater risk to families, and also prolonged stay in destination cities compound the risk of psychiatric disorders.[9,11-13] The construction industry has high levels of occupational strain, physical and mental health problems could arise related to work and the environment of the workplace.[14,15] Studies revealed that the threat to mental health depends on the social context, the circumstances, and the act of migration.[16,17]

Even though distressed people migrate to cities for temporary work in the construction sector, moving from distressing conditions to another stressful job will obviously lead to more psychological distress. In this context, the migratory construction workers might encounter and develop more distress. The unaddressed distress aspect can lead to mental health problems among migrants. Hence, assessing their level of distress and quality of community life (QoCL) such as living conditions, accessibility with civic amenities, availability of social support, and information about social services other aspects are important. By understanding this problem, the mental health professionals could design, develop, and deliver the appropriate community mental health services by following intersectoral services.

The dimension of the QoCL, which is the perception of “being,” “belonging,” and “becoming” a part of one’s community, may also have consequences for mental morbidity.[18] Indian Council for Medical Research (ICMR) specifies that QoCL is the assessment of the quality of life within a community by understanding a member’s point of view related to the individual, family, and community social support systems.[19]

Due to the COVID-19 pandemic, the migrant population has encountered various psychosocial stressors in the early phase of the lockdown. The entire country has noticed the vulnerability of the migrants, mainly daily wagers, construction, and other unorganized sector workers. They encountered several psychosocial stressors, such as living in camps, lack of privacy, maintaining physical distance, lack of safety, uncertainty about the duration of the lockdown, fear of losing their jobs, loss of income, social needs of their children and pregnant women, and the urgency to travel to their hometowns, occupational pneumoconiosis, tuberculosis, absence of family support and lack of caretakers during the emergency, social segregation, and inability to access the health and psychiatric services and other aspects have heightened the distress among migrant workers.[20,21]

All the above factors associated with migration increase their distress and limit the social support in destination places, it leads to poor quality of life among migratory construction workers. Most research has broadly focused on the issues of migratory workers, but their distress and QoCL aspects have been paid little attention.

MATERIALS AND METHODS

The present study was carried out to assess the psychological distress and QoCL of migratory construction workers. The cross-sectional research design was adopted and a survey method of sampling was followed. The Chandapura Panchayat (a group of revenue villages), Anekal Taluk of Bangalore urban district, was considered the universe of study.

In Chandapura panchayat, numerous construction projects were ongoing. One such construction place is a Surya City Layout. The persons who migrated and working as manual/ semi-skilled laborers at construction work, and resided at construction sites or premises/camps, were considered the population of the study. These migrant camps are called Gulbarga camps, Raichur camps, and other names of Northern parts or states of India. Depending on their convenience, comfortability, availability of a job, or better wages, the migrants would stay in these camps/sites for some weeks to months.

Among these camps, one of the Gulbarga camp was selected randomly for the present study. The study constituted the total enumeration of all the individuals living in the Gulbarga camp. During the first visit of the study, it was found that the Gulbarga camp comprises 75 households/sheds, and approximately 300 migrant adults residing in these households. The adult migratory construction workers, who could speak Kannada and Telugu languages, were included in the study. Only one respondent from each household unit was selected for the study. Out of 300 migrant workers, nearly 70 were not included in the study for the reasons of not working in the construction sector, especially females (lactate mothers, working at garments, and housemaids), and senior citizens (taking care of their grandchildren). The remaining 110 persons had not shown interest in participating in the study. The other 12 persons reported their pre-existing neuropsychiatric disorders, such as headache, anxiety, depression, and obsessive and compulsive disorders. Another 33 were not included due to their unavailability during the field visit despite having pre-appointments and coordination with their family members and other concerned persons. Finally, 75 migrants were considered as the sample of the study. The data collection was carried out on Sundays.

A semi-structured interview schedule was prepared to determine the respondent’s sociodemographic details and other aspects of migration. A Self-reporting Questionnaire-20 (SRQ-20) developed by the World Health Organization was applied to assess psychological distress.[22] The SRQ-20 items were scored through “yes” (score is 1) or “no” (score is 0) responses. A score of “1” indicates that the symptom was present in the previous month. SRQ’s cutoff point was 7/8 out of 20. A score below 7 was considered a non-clinical case, while an eight and above score indicated a clinical case and required psychosocial interventions.

The QoCL questionnaire was developed by the ICMR.[19] The QoCL scale consisted of 11 factors, with a range of scores for each factor is 3–9. Each factor contained three questions and a total of 33 questions, and the total score range for all the factors is 33–99. The average score of a single factor is “6,” and for all factors, the overall average score is ‘“66’,” kept as a cutoff for determining the QoCL. The higher the score, the greater the QoCL.

All these tools have been translated into Kannada and Telugu languages. A total of 75 respondents were interviewed for the study. The data were analyzed using SPSS-16 version software. The frequency distribution and descriptive statistics were used to analyze the respondents’ sociodemographic status, migration details, level of distress, and QoCL. A t-test was computed to determine the significance of gender differences between distress and QoCL domains. The Pearson correlation test was administered to find out the relationship between SRQ-20 (distress), QoCL, and other variables.

Ethical clearance was obtained from the Institutional Ethics Committee (Behavioral Science Division), NIMHANS, Bengaluru. Informed consent was obtained from each respondent for their participation.

RESULTS

Sociodemographic profile of the migrants

The mean age of the respondents was 32.03 ± 9.82 years and the range was 19–55 years. Most of the respondents 53 (70.7%) were men. About 59 (78.7%) were married, 16 (21.3%) were single, and one was a widower (1.3%).

Regarding their educational status, 37 (49.3%) respondents did not have formal education, 24 (32.0%) had studied up to fourth standard, and 14 (18.7%) had studied up to high school and above. Furthermore, it was found that 23 (30.7%) respondents’ monthly income was between ₹5000 and ₹7999, and another 42 (56.0%) respondent’s monthly income was ₹8000–10,999.

Migration details and reasons for migration

The duration of migration showed that 38 (50.7%) respondents migrated for less than a month and 28 (37.3%) migrated for 2–6 months. The remaining 9 (12.0%) respondents migrated for 7 months and above period. Nearly 15 (20.0%) respondents had migrated alone, 33 (44.0%) had migrated with their spouses and children, 4 (32.0%) had migrated along with their spouses, and another 3 (4.0%) had migrated with all their family members, including elders. The respondents have reported multiple reasons for migration, such as 51 (68.0%) had a drought in their area, 48 (64.0%) facing debts, 48 (64.0%) having poverty, 41 (54.7%) had unemployment, 40 (53.3%) had a loss of capital investment, 39 (52.0%) had low wages, and 20 (26.7%) reported that family issues caused for their migration.

Item-wise distribution of psychological distress (SRQ-20)

The item-wise distribution of factors related to SRQ-20 showed that 83% of respondents had felt unhappy and nervous, 75% had sleep disturbances, 71% had trouble thinking, 69% had felt easily tired and often had headaches, 65% had lost interest in doing things, 64% had difficulty in making decisions, and 60% had poor appetites. It also noted that 59% had felt tired, 47% suffered from daily work, and 53% had an uncomfortable feeling in their stomachs. The respondents also reported that 44% had to shake hands, 40% had difficulty enjoying daily activities, 40% had felt like worthless persons, 39% had poor digestion, 39% had thought of ending their lives, 35% were easily frightened, and 29% had been crying more than usual.

Level of distress (SRQ-20)

The respondents’ distress level showed that 48 (64.0%) had scored more than 8, and 27 (36.0%) scored < 7 out of a total possible score of 20. The respondents with a distress score of 8 and above required psychosocial care and support.

[Table 1] shows the domain-wise mean scores of the QoCL. The minimum level of QoCL noticed in the domain of community efforts for sanitation was (3.26 ± 0.60); above the moderate level QoCL was seen in the domains of caste and religion (7.06 ± 1.29) and law and order problems were (7.04 ± 1.28). The overall QoCL was (M = 59.08 ± 8.31) in the range of 44.00–80.00.

Table 1:: Details of the mean and standard deviation of the domains of QoCL (n=75).
S. No. Domains Mean SD Range*
(Min–Max)
1. Community efforts for sanitation 3.26 0.60 3.00–6.00
2. Social discrimination 4.60 1.10 3.00–7.00
3. Support of relatives 4.86 1.69 3.00–9.00
4. Relationships with colleagues 5.46 1.38 3.00–9.00
5. Support of family 5.29 1.78 3.00–9.00
6. Support of neighbors 5.38 1.49 3.00–8.00
7. Relationships with friends 5.36 1.36 3.00–8.00
8. Medical and other facilities 5.33 0.92 3.00–9.00
9. Social contacts and community information 5.40 1.68 3.00–9.00
10. Law and order problems 7.04 1.28 3.00–9.00
11. Caste and Religion 7.06 1.29 3.00–9.00
Total 59.08 8.31 44.00–80.00
In each domain, the minimum possible score is 3, and the maximum possible score is 9. QoCL: Quality of community life

[Table 2] describes the significant differences between gender, distress, and domains of QoCL. It showed that female respondents (M = 12.90 ± 4.03, t = −3.03, P < 0.003) had higher distress levels compared with male (M = 9.50 ± 4.56, t = −3.03, P < 0.003) respondents. The QoCL showed that there was a significant difference between male and female workers in the domain of support of relatives (t = 2.828, P = 0.003); support of family (t = 3.068, P = 0.003); social contacts and community (t = 2.80, P = 0.007); and social discrimination (t = 1.92, P = 0.059). The overall QoCL indicated that males (60.73 ± 8.13, t = 2.79, P < 0.007) had better QoCL than females (55.09 ± 7.48, t = 2.79, P < 0.007) workers. The t-test showed no statistically significant difference (P > 0.05) in the other domains of QoCL.

Table 2:: Gender differences in distress, QoCL domains, and independent sample t-test.
Variable Gender N Mean SD t-value df P-value
Distress (SRQ-20)
Distress Male 53 9.50 4.56 −3.03 73 <0.003
Female 22 12.90 4.03
Total 75 10.50 4.65
QoCL domains
Support of relatives Male 53 5.20 1.72 2.828 73 0.006
Female 22 4.04 1.32
Support of family Male 53 5.67 1.70 3.068 73 0.003
Female 22 4.36 1.64
Social contacts and community Male 53 5.73 1.71 2.80 73 0.007
Female 22 4.59 1.33
Social discrimination Male 53 4.75 1.09 1.92 73 0.059
Female 22 4.22 1.06
QoCL total Male 53 60.73 8.13 2.79 73 0.007
Female 22 55.09 7.48

SRQ-20: Self-reporting questionnaire-20, QoCL: Quality of community life

Table 3 shows the Pearson correlation test results. The age of the respondents was positively correlated with distress (r = 0.323, P < 0.01). Age was negatively correlated with QoCL domains such as relatives (r = −0.283, P < 0.05), family (r = −0.350, P < 0.01), neighbors (r = −0.361, P < 0.01), and total QoCL (r = −0.299, P < 0.01).

Table 3:: Pearson correlation test details between QoCL, SRQ -20, and other variables.
Variable Age Days of work Total SRQ Colleagues Community Relatives Family Neighbors Friends Medical and social Social contact Law and order Caste and Religion Social discrimination Total QoCL
Age 1.00
Days of work 0.183 1.00
Total SRQ 0.323** 0.076 1.00
Colleagues −0.190 0.074 −0.463** 1.00
Community efforts −0.040 0.117 0.009 0.238* 1.00
Relatives −0.283* −0.107 −0.681** 0.549** 0.115 1.00
Family −0.350** 0.043 −0.727** 0.468** 0.128 0.648* 1.00
Neighbors −0.361** 0.014 −0.641** 0.614** 0.124 0.681** 0.635** 1.00
Friends −0.223 0.000 −0.453** 0.324** 0.063 0.320** 0.456** 0.494** 1.00
Medical and other facilities −0.144 0.003 −0.201 0.331** 0.057 0.193 0.236* 0.249* 0.108 1.00
Social contacts −0.118 0.135 −0.548** 0.410** 0.040 0.544** 0.495** 0.586** 0.402** 0.113 1.00
Law and Order 0.050 −0.006 0.062 −0.011 0.056 0.120 0.001 −0.015 0.030 −0.034 0.024 1.00
Caste and Religion 0.023 0.004 0.044 −0.077 −0.162 −0.100 0.067 0.084 0.139 0.241* 0.012 0.095 1.00
Social discrimination −0.011 0.111 −0.049 0.018 0.020 0.325** 0.102 0.021 −0.029 0.093 0.225 0.306** 0.094 1.00
Total QoCL −0.299** 0.023 −0.698** 0.668** 0.196 0.795** 0.774** 0.807** 0.590** 0.390** 0.711** 0.241* 0.220 0.342** 1.00
Correlation is significant at the 0.01 level (two-tailed), *Correlation is significant at the 0.05 level (two-tailed). SRQ-20: Self-reporting questionnaire-20, QoCL: Quality of community life

The total SRQ-20 was negatively correlated with colleagues (r = −0.463, P < 0.01), relatives (r = −0.681, P < 0.01), family (r = −0.723, P < 0.01), neighbors (r = −0.641, P < 0.01), friends (r = −0.453, P < 0.01), social contacts (r = −0.548, P < 0.01), and total QoCL (r = −0.698, P < 0.01) of the respondents.

The domains of colleagues on the QoCL were positively correlated with other domains of QoCL, such as community efforts (r = 238, P < 0.05), relatives (r = 0.549, P < 0.01), family (r = 0.468, P < 0.01), neighbors (r = 0.614, P < 0.01), friends (r = 0.324, P < 0.01), medical and other facilities (r = 0.331, P < 0.01), social contacts (r = 0.410, P < 0.01), and total QoCL (r = 0.668, P < 0.01). The domain of relatives in QoCL was positively correlated with other QoCL domains such as family (r = 0.648, p < 0.05), neighbors (r = 0.681, P < 0.01), friends (r = 0.320, P < 0.01), social contacts (r = 0.544, P < 0.01), social discrimination (r = 0.325, P < 0.01), and total QoCL (r = 0.795, P < 0.01) of the respondents.

The family domain of QoCL was positively correlated with neighbors (r = 0.635, P < 0.01), friends (r = 0.456, P < 0.01), medical and other facilities (r = 0.236, P < 0.05), social contacts (r = 0.495, P < 0.01), and total QoCL (r = 0.774, P < 0.01). The neighbor’s domain was positively correlated with friends (r = 0.494, P < 0.01), medical and other facilities (r = 0.249, P < 0.05), social contacts (r = 0.586, P < 0.01), and total QoCL (r = 0.807, P < 0.01) of the respondents.

The friend’s domain was positively correlated with social contacts (r = 0.402, P < 0.01) and total QoCL (r = 0.590, P < 0.01) of respondents. The medical and other facilities domain was positively correlated with caste and religion (r = 0.241, P < 0.05) and total QoCL (r = 0.390, P <0.01). Similarly, the social contact domain was positively correlated with overall QoCL (r = 0.711, P < 0.01). The domain of law and order was positively correlated with social discrimination (r = 0.306, P < 0.01) and overall QoCL (r = 0.241, P < 0.05). The domain of social discrimination of QoCL was positively correlated with overall QoCL (r = 0.0.342, P < 0.01). The other domains, such as days of work and community efforts, were not correlated with the domains of QoCL.

DISCUSSION

In low- and middle-income countries, internal and seasonal migration is a survival strategy for many individuals and families, especially agricultural laborers and poor income groups. In the present study, the mean age of the respondents was 32 ± 9.82 years. Other studies also reported more or less similar findings, such as 26.25 ± 8.49 years and 26 ± 8.2 years.[12,23]

In this study, most of the respondents, that is, 53 (70.7%) were male, 59 (78.7%) were married, 37 (49.3%) did not have formal education, and 65 (86.7%) of the respondent’s monthly income was between ₹5000 and ₹10,999. Similarly, other studies have reported that most of the migrant construction workers are male (95.2%), unskilled (79.4%), seasonal workers, belong to poor socioeconomic backgrounds, and are illiterates.[2,12,24,25] However, the respondents’ monthly income is more or less similar as mentioned in the minimum wages act. As per the minimum wages act, the semi and unskilled worker’s monthly income is about ₹7000-00 in Zone-1 cities (Bengaluru).[26]

The present study found that 50.7% had migrated for less than a month, and another 37.3% had migrated for 1–6 months. Studies reported that construction workers have maximum mobility because of the nature of their work.[5,6,27] In the present study, the migrant workers worked daily or weekly basis and did not follow any contractual work period. Hence, this could be a reason for the less duration of the migration. In this study, 57 (76.0%) of the respondents migrated with a spouse, or spouse and children. Studies found that migration along with the family could facilitate better psychosocial support.[13,25]

The distress is higher among the male (9.50 ± 4.56) than the female (12.90 ± 4.03) respondents. In this way, in terms of age group, gender, population category, and migration status. Studies have reported that distress is highest between 18 and 29 years age group population,[25] ranging between 5% and 27% in the general population,[28,29] and 13–39% among the immigrants.[30] The prevalence of distress is higher in women than men.[31-33] Other studies also noted that a considerable proportion of construction workers had the symptoms of common mental disorders and post-traumatic stress disorders due to life events.[34,35]

However, the other sector workers (non-construction workers) also have higher stress levels in the country. The Marsh India (insurance company) study found that 59% of employees in India reported feeling stressed in everyday life, which is at higher levels than the global average in the post-pandemic (COVID-19) phase.[36]

In this study, respondents reported higher distress levels. It might be possible that pre-migratory (crop loss, drought, debts, loss of capital investments, poverty, and unemployment), and post-migratory factors (adjustment at a destination place, temporary jobs, lacking basic amenities, and living at construction sites) might lead to higher levels of distress. Contrary to this, a study on migratory quarry workers reported that only 16.5% of respondents had higher distress.[37] It might be possible that they were quarry workers by occupation; they have been working in quarry sites and might have adjusted to the lifestyle and nature of the work. A mixed-method study reported that construction workers had higher psychological and occupational hazards, which are also affecting their well-being and decreasing their construction work performance.[38]

The majority of respondents have below moderate QoCL (59.08 ± 8.31) and the gender-wise difference shows that men (60.73 ± 8.13) have a better QoCL compared to women (55.09 ± 7.48). With this connection, another study reported poor quality of life (55.9 ± 3.7) among construction workers.[24] Furthermore, studies have revealed that migration brings numerous stressors, including job uncertainty, poverty, social and geographic isolation, intense time and work pressures, poor housing conditions, separation from family, lack of recreation, poor health, shelter, and safety concerns.[9,10]

Other studies found that adequate wages for construction workers were not competitive to satisfy the needs of the present economic situation. At the same time, gender discrimination is widespread and lack of support at the workplace in the construction industry.[39-42] Studies also reported that construction workers turned to substance abuse as a diversion from dealing with work stress and substance abuse was associated with anxiety.[3,43] Thus, our study also noted that all these factors might have contributed to the migrants having higher distress levels and poor QoCL.

Recommendation/suggestions

In this study, nearly 2/3 of workers have higher distress and their QoCL is moderate level; most of them belong to a younger age group, with the potential aspect to support their family and the development of the nation. It shows that psychosocial support is required for migratory construction workers. The study opines that public health and mental health professionals had to take up initiatives to address health needs in terms of the bio-psycho-social aspects of migrants.

The collaborative services of the government and other voluntary organizations would help to improve the QoCL factors, such as adequate shelter care, improving sanitation and hygiene, information about social services and other amenities, safety and protection, education for children, recreational programs, screening of health and mental health problems, helpline services, training in vocational skills, accessing food grains through the public distribution system under the one nation, one ration card system, and other similar aspects. Pre-migration training is required to build effective coping skills, which prepare for the process of migration. Initiation of peer group services could help them to have emotional and informational support.

Limitations

The tools of the study have not been validated into local vernacular. The sample size of the study is small, which limits the generalizability of the findings.

CONCLUSION

The present study showed that migratory construction workers had a higher level of distress, and their QoCL was below moderate. It was observed that pre-and post-migration factors such as poor living and working conditions, lower wages, job uncertainty, and lack of social security schemes might create psychosocial stress. The stress led to distress and resulted in poor QoCL. Intersectoral approaches have been required at the primary, secondary, and tertiary care levels to reduce distress and enhance the QoCL of the migratory construction workers.

Declaration of patient consent

The authors certify that they have obtained all appropriate patient consent.

Conflicts of interest

There are no conflicts of interest.

Financial support and sponsorship

The first author has received a scholarship from the NIMHANS for his pre-doctoral (M. Phil) study.

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