Abstract
Objectives
This paper explores the prevalence and factors contributing to depression among truck drivers in India. The aim is to identify specific factors associated with depression in this population and to provide suggestions for mitigating these factors.
Methods
The study employs an observational cross-sectional analytical approach to explore truck drivers on National Highways through Jaipur, Rajasthan. It explored how work routine, workspace conditions, and family/social engagement impact depression. Variables included work hours, rest breaks, driving conditions, and social factors like police harassment. A sample of 300 drivers was selected using Probability Proportional to Size sampling from four randomly chosen halt points, based on average truck traffic, meeting criteria like experience and vehicle type.
Results
The study surveyed truck drivers aged 22–56 years (average 35, SD = 6.6) and identified significant associations between depression and factors like working hours (χ2 = 51.227, p = .000), police humiliation (χ2 = 21.740, p = .000), workspace distraction (χ2 = 89.463, p = .000), and uncomfortable workspace conditions (χ2 = 7.997, p = .005). Age (χ2 = 3.083, p = .079) and marital status (χ2 = 1.782, p = .182) were not significant. Job satisfaction (B = 1.813, p = .001) and supervisor support (B = 1.156, p = .018) were significantly linked to depression.
Conclusions
The study concludes that multiple factors significantly influence the likelihood of experiencing depression among truck drivers. Based on these findings, several recommendations are proposed to help reduce depression rates in this population. These include making mental health services readily available and accessible, implementing strict regulations on working hours to prevent excessive fatigue, and preventing police humiliation and abuse. This research contributes to the growing concern about the mental health of truck drivers in India and underscores the need for effective interventions to address these issues comprehensively.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-025-22370-7.
Keywords: Depression, Heavy truck, Job satisfaction, Loneliness, Mental health, Police humiliation, Truck drivers, Work environment
Introduction
The trucking industry heavily relies on the dedication and resilience of truckers who play an important role in ensuring smooth transportation and timely delivery of goods between two long distances. Truck Drivers can be considered one of the most important pillars of the logistic sector but are susceptible to risks due to the disjointed nature of the trucking industry.
Usually, school dropouts or men from poor families join this profession of truck driver initially start as a cleaner and end up learning drive to be a driver. Mostly drivers not the owner of the truck and they work per day for an average of 12.7 h, which effect their quality of life and health [1]. They are poorly paid and have no adequate time for their food and sleep/rest. According to the economic research institute, the average annual salary range for a Heavy Truck Driver in India is between INR 359,266 and INR 594,972, including a bonus [2]. Truck drivers depend on the roadside food stall and eateries for their foods.
Mostly truck drivers earn their wages between 10,000 to 20,000 Indian rupees a month. Their living conditions are appalling; there is no wage uniformity, no social protection, and no rewards for completing a trip on time. They often suffer from driver fatigue due to extensive working hours. On average, each driver drives for about 11.9 h a day. In terms of average distance covered, a truck driver covers approximately 417 km daily. As per the safe live foundation drivers they drive vehicles even if they feel fatigued or sleepy [3]. Long work hours, infrequent breaks, irregular scheduling, and tight delivery or tour schedules can all contribute to truck drivers becoming exhausted and stressed out at work.
Depression is a common and serious mental health condition characterized by persistent feelings of sadness, hopelessness, and a lack of interest or pleasure in activities. It can affect a person's thoughts, emotions, and physical well-being, impacting their ability to function in daily life. The National Institute of Mental Health (NIMH) is a leading research organization dedicated to understanding, treating, and preventing mental health disorders. It operates under the U.S. National Institutes of Health (NIH) and is the largest scientific organization in the world focused on mental health research. NIMH, offers an overview of depression, covering its symptoms, risk factors, and available treatment options. The information is accessible to the general public, promoting mental health awareness [4].
Depression is a complex condition with various contributing factors, and its severity can range from mild to severe. The impact of depression extends beyond the individual, influencing relationships, work performance, and overall quality of life. The World Health Organization's comprehensive report (2017) offers a global perspective on the prevalence of depression and common mental disorders [5]. It highlights the significant burden of these conditions on public health and emphasizes the need for effective interventions.
Depression among truck drivers globally
Depression is a pervasive mental health issue that affects individuals across various professions, and truck drivers represent a demographic particularly vulnerable to its impact. Long hours, extended periods of isolation, irregular sleep patterns, and the demanding nature of the job contribute to the unique challenges faced by truck drivers, making them susceptible to mental health issues, particularly depression. This literature review aims to explore the existing research on depression among truck drivers, highlighting key factors, consequences, and potential interventions.
Data on mental health disorders among commercial drivers are sparse, but mental illnesses, such as depression, anxiety and mood disorders, were ranked among the lowest of importance as safety issues in a survey of US carrier safety managers [6].
Depression among truck drivers in India
In recent years, there has been a growing concern about the mental health of truck drivers in India, given the unique challenges they face in their occupation.
Occupational challenges
Truck drivers in India operate in a demanding and stressful environment. Long hours of continuous driving, unpredictable road conditions, and exposure to extreme weather conditions contribute to a high-stress work environment. The irregular schedules and extended periods away from home also lead to disruptions in sleep patterns, social isolation, and strained family relationships [7].
Socioeconomic factors
The socioeconomic status of truck drivers in India is often precarious, with low wages, job insecurity, and limited access to healthcare facilities. Economic instability may contribute to heightened stress and anxiety, further exacerbating the risk of depression among this population.
Substance abuse
Substance abuse, particularly alcohol and stimulant use, is a prevalent issue among truck drivers in India. The use of these substances is often a coping mechanism for the stress and fatigue associated with long-haul driving. Substance abuse not only contributes to the development of depression but also poses additional risks on the road. [8].
Limited access to mental health services
Truck drivers in India often face challenges in accessing mental health services due to the transient nature of their work. Limited awareness, social stigma, and the lack of mental health resources along highways contribute to the underdiagnosis and undertreatment of depression in this population [9].
This section aims to provide a broad overview of depression, drawing on key references that explore its prevalence, contributing factors, and the diverse strategies for prevention and treatment.
Aspects of depression
Persistent of sadness or low mood and Loss of interest or pleasure: Individuals experiencing depression often feel sad, empty, or down most of the time. This low mood is not necessarily related to a specific event and may persist for weeks or months.
Anhedonia, or the inability to experience pleasure from activities that were once enjoyable, is a common symptom of depression. Hobbies, social interactions, and other activities may no longer bring satisfaction.
Consequences of depression
Depression can lead to significant changes in various aspects of life, including alterations in sleep patterns and appetite or weight. Individuals often experience persistent fatigue and low energy levels, coupled with difficulty concentrating on tasks. Feelings of worthlessness or excessive guilt are common, which can severely impact self-esteem and daily functioning. Additionally, depression can cause noticeable changes in movement, such as restlessness or slowed physical activities. In severe cases, individuals may have suicidal thoughts, highlighting the critical need for effective mental health interventions (Fig. 1).
Fig. 1.
Consequences of depression
Literature review
Research indicates that the trucking industry's work environment is characterized by various stressors. These include being socially isolated and feeling lonely, having stressful financial obligations [10] The road infrastructure quality and traffic conditions like inadequately maintained roads, traffic congestions, and poor traffic management, inadequate signages impact the trucking operations significantly contributing to delays, increased fuel consumption and elevated stress levels along with truck drivers [11, 12] A study by Reddy and Sudhakara (2019) explores the impact of extended working hours on driver fatigue and its correlation with road accidents.
The well-being of truck drivers in India is influenced by socio-cultural factors, including lifestyle, living conditions, and access to healthcare [13]. A study by Gupta et al. (2020) investigates the prevalence of health issues among truck drivers, including musculoskeletal problems, obesity, and sleep disorders [7]. The demanding driving environment in India has been associated with elevated levels of stress, anxiety, and depression among truck drivers [7].
Extended periods spent traveling result in isolation, limiting social interactions. This lack of social support has been linked to increased levels of depression among truck drivers [14]. The rigorous demands of the work, which include strict deadlines, traffic challenges, and pressure to meet deadlines, contributes to chronic stress among truck drivers, which is closely associated with depression. The irregular schedules and extended periods away from home also lead to disruptions in sleep patterns, social isolation, and strained family relationships. Research also depicts that economic instability may contribute to heightened stress and anxiety, further exacerbating the risk of depression among this population.
Objectives
The primary objective of this study is to study and understand the potential association between the work environment experienced by truck drivers and the prevalence of depression within this occupational group. By delving into the various aspects of their work environment, including but not limited to factors such as workload, working hours, job demands, social support, and organizational culture, we aim to discern how these elements may influence the mental well-being of truck drivers. Through a comprehensive assessment of these variables, we seek to elucidate any correlations or patterns that may exist between the work environment and depression among truck drivers. By identifying and understanding these associations, our research endeavours to contribute valuable insights that can inform policies, interventions, and support systems aimed at promoting the mental health and overall well-being of truck drivers, ultimately fostering safer and healthier work environments within the trucking industry.
Methods
Study design
The study applied an observational cross-sectional analytical approach to explore targeted facets. Data collection cantered on truck drivers navigating freight routes along key National Highways interconnected through Jaipur, Rajasthan.
Study area
The study's geographical scope centers on truck drivers navigating freight routes that span major National Highways connected via Jaipur, Rajasthan. This deliberate selection of the region implies a thoughtful consideration, likely influenced by the decisive role these routes play in freight transportation or other pertinent factors.
Variables
The diagram illustrates the relationships between various independent variables and the dependent variable of depression, highlighting how work and social factors can influence mental health. The independent variables are categorized under three main groups: work routine and working hours, workspace conditions, and family and social engagement. Work routine and working hours encompass aspects such as duration, rest breaks, shift patterns, and the provision of leave. This category also includes leisure time, the role in loading and unloading (which adds extra burden beyond driving), and the behavior of company staff. Workspace conditions involve factors like the driving cabin and seating posture, availability of rest areas, and environmental conditions such as heat, noise, and vibration. Family and social engagement, including police harassment, is another crucial factor. All these independent variables collectively impact the dependent variable, which is depression, suggesting a complex interplay between occupational, environmental, and social factors in the mental well-being of individuals (Fig. 2).
Fig. 2.
Independent and dependent variables
Variables
Dependent variable
The dependent variables be the outcome variable Depression. Dependent variables, also known as outcome variable, refer to the variables in an conduct test or study that are being measured or observed in response to changes made to one or more independent variables. In this case, depression are the dependent variable of interest. Symptoms of depression include constant feelings of sadness, hopelessness, and worthlessness, loss of interest in activities, difficulty sleeping, and changes in appetite and energy levels. Both fatigue and depression can significantly impact a person's quality of life, making it essential to understand their underlying causes and potential interventions.
These individuals have scores between 10 and 63 on the Beck Depression Inventory, suggesting the presence of moderate to severe depression symptoms. It is essential to note that the cutoff points for minimal depression and depression were calculated based on the scoring system of the Beck Depression Inventory. The specific ranges used in this study were 0–9 for minimal depression and 10–63 for depression.
Independent variables
The independent variable will be the background variables that constitute work environment. Including age, marital status, working hours, police humiliations, workspace conditions, workspace distractions, social engagement, job satisfactions etc.
Sample size
To study the work environment on depression among truck drivers, the sample size was meticulously determined. For this study, four halt points were randomly selected. Subsequently, the sample size for each halt point was calculated using the Probability Proportional to Size (PPS) sampling method, which was based on the average truck-traffic load observed over the past week. The study ultimately included a total sample of 300 truck drivers, all meeting specific inclusion criteria: main occupation as truck driving, operation of a heavy truck with three or more axles, and a minimum of 12 months' experience in driving heavy trucks. A selective inverse sampling approach was employed to choose the sample from the identified halt points.
Selection of halt point
Firstly, the selection of truck drivers' key transportation/trucking hubs was done and based on available data (online) and discussions with truck transport associations across Jaipur City (Transport Nagar, VKI area, etc.) and nearby places, halt points were identified. For the purpose of this study, four halt points were identified randomly, and the sample size for each halt point was determined using Probability proportional to size (PPS) sampling method based on the average truck-traffic load in the last one week. According to PPS, a proportionate sample was selected as per the probability of the available population, and the sample size was chosen. Thus, a larger sample size was selected from the halt points with a higher population (truck-traffic load), and a smaller proportion of the sample was selected from halt points with a lower truck traffic load. An inverse sampling procedure was used for selecting the sample from halt points.
In inverse sampling, we continue to choose items until an event has occurred a specified number of times [15].
The data was collected from the halt points until the total sample according to inclusion criteria was reached. As a sample size of 300 was needed to be captured, data collection from all selected halt points was continued until the required number as per the inclusion criteria was reached.
Method of data collection
A structured questionnaire was employed to gather data from truck drivers, acquired through face-to-face interviews conducted at designated halt points along the specified freight routes. All truck drivers making stops at roadside eateries and rest areas along National Highways or in the outskirts of the city were invited to partake in the study. The structured questionnaire used for assessing depression employed the Beck Depression Inventory a globally recognized tool known for its reliability and construct validity (Test–retest reliability: r = 0.93; Construct validity: α = 0.92) [16]. The questionnaire for assessing the work environment was developed, pretested, and received approval from the Institutional Research Board of IIHMR University, Jaipur, India (Annexure-I).
Data analysis and scoring methods
The data underwent analysis using SPSS 25 software. Descriptive statistics were computed, and a multivariable logistic regression analysis was conducted to ascertain the relationship between the dependent and independent variables [17].
Ethical concerns
The protocols and data collection tools were implemented following approval from the Institutional Review Board IIHMR University, Jaipur (reference no IIHMR-U/IRB/2022/2). Prior to participation, informed consent was obtained from all respondents, emphasizing their right to choose whether to answer all or only some of the questions. Rigorous measures were taken to safeguard the privacy and confidentiality of respondents' information.
Results and discussions
Profile of the respondents
The demographic analysis of respondents in this study reveals a varied age distribution, with approximately one-third falling within the 20–30 age group, one-fourth in the 36–40 age bracket, and around one-fifth aged over 40 years. The average age of respondents is 35 years, with a range spanning from 22 to 56 years and a standard deviation of 6.6. Additionally, the study highlights a high prevalence of marriage among respondents, with approximately 80% currently married (Fig. 3).
Fig. 3.
Marital status of the respondents (%)
Association between depression and predictor variables
The chi-square analysis uncovered a correlation between age and fatigue. Notably, individuals aged 35 and above exhibited lower susceptibility to fatigue in contrast to their counterparts under 35 years old. However, the relationship between age and depression did not reach statistical significance (χ2 = 3.083, p = 0.079) (Table 1).
Table 1.
Association between age and depression
| Age of the respondents | < 35 years (n = 140) |
> 35 years (n = 160) |
Total (n = 300) |
χ2 | p |
|---|---|---|---|---|---|
| Depression | |||||
| Depression | 11.4% | 18.8% | 15.3% | 3.083 | .079 |
Depression: Minimal depression (0–9); Depression (10–63)
The chi-square analysis indicated a lack of significant correlation between marital status and depression (χ2 = 1.782, p = 0.182). Interestingly, a higher proportion of individuals experiencing depression were observed to be married compared to those who were separated or never married (Table 2).
Table 2.
Association between marital status and depression
| Marital Status of the TD | Separated/ never married (n = 61) |
Currently Married (n = 239) |
Total (n = 300) |
χ2 | p |
|---|---|---|---|---|---|
| Depression | |||||
| Depression | 9.8% | 16.7% | 15.3% | 1.782 | .182 |
Depression: Minimal depression (0–9); Depression (10–63)
Working hours and depression
The data from Table 3 unveils insights into the driving patterns of truck drivers, indicating that they operate vehicles throughout both day and night, with a combined driving duration of 12 h per day. Further analysis reveals a nearly equal distribution of driving time, with drivers spending an average of approximately six hours behind the wheel during both daytime and nighttime. This suggests a balanced allocation of driving duties between day and night shifts. Moreover, the data delves into the continuous driving behaviors of truck drivers, illustrating an average duration of 4.1 h spent driving continuously. These findings underscore the diverse workloads experienced by truck drivers and emphasize the significance of considering both rest intervals and average driving hours to optimize working conditions and promote the mental well-being of truck drivers, particularly in the context of depression.
Table 3.
Average truck driving time, and driving in one stretch among truck drivers
| Indicators | % |
|---|---|
| Average hours of driving truck in a day | |
| Average hours truck drivers drive trucks each day in daytime | 5.5 |
| Average hours truck drivers drive trucks each day in nighttime | 6.0 |
| Total | 11.6 |
| Average hours of driving truck in one stretch in a day | |
| Average hours truck drivers drive trucks each day in one stretch in daytime | 3.9 |
| Average hours truck drivers drive trucks each day in one stretch in nighttime | 4.4 |
| Average (Total) | 4.1 |
The chi-square analysis unveiled notable connections between working hours and depression, signifying a significant relationship (χ2 = 51.227, p = 0.000). Specifically, truck drivers clocking in 12 h, or more were at a heightened risk of experiencing depression in comparison to those with less than 12 h of work (Table 4).
Table 4.
Association between working hours and depression
| Working hours of truck driver | Less than 12 h’ drive (n = 192) |
12 Hours and more drive (n = 108) | Total (n = 300) |
χ2 | p |
|---|---|---|---|---|---|
| Depression | |||||
| Depression | 4.2% | 35.2% | 15.3% | 51.227 | .000 |
Depression: Minimal depression (0–9); Depression (10–63)
Police humiliation and depression
Police humiliation encompasses a spectrum of negative encounters between law enforcement and truck drivers, ranging from verbal abuse and unwarranted citations to discriminatory treatment and harassment. These experiences can lead to feelings of powerlessness, frustration, and indignity, contributing to psychological distress and potentially exacerbating preexisting mental health issues.
The chi-square analysis revealed a noteworthy correlation between instances of police humiliation or abuse and depression, underscoring a statistically significant relationship (χ2 = 21.740, p = 0.000). Those subjected to humiliation or abuse by the police, even in cases of compliance with regulations, exhibited a higher likelihood of reporting depression compared to individuals who did not experience such treatment (Table 5).
Table 5.
Association between police humiliation and depression
| Humiliation/abuse from police even if comply rules | Not hamulated from police (n = 178) |
Humiliation/ abuse from police (n = 122) | Total (n = 300) |
χ2 | p |
|---|---|---|---|---|---|
| Depression | |||||
| Depression | 7.3% | 27.0% | 15.3% | 21.740 | .000 |
Calculation: depression: Minimal depression (0–9); depression (10–63)
Workspace distraction and workspace condition
Workspace distractions have been linked to an increased risk of accidents, near-misses, and traffic violations among truck drivers. The constant barrage of stimuli can impede attentional focus, reaction times, and decision-making abilities, heightening the likelihood of errors and safety incidents on the road.
The chi-square analysis unveiled notable connections between workspace distraction and depression, indicating a significant relationship (χ2 = 89.463, p = 0.000). Individuals grappling with workspace distraction were notably more inclined to report heightened levels of depression compared to those without such distractions (Table 6).
Table 6.
Association between workspace distraction and depression
| Workspace distraction | No perceived workspace distraction (n = 252) |
Workspace distraction present (n = 48) |
Total (n = 300) |
χ2 | p |
|---|---|---|---|---|---|
| Depression | |||||
| Depression | 6.7% | 60.4% | 15.3% | 89.463 | .000 |
Depression: Minimal depression (0–9); Depression (10–63)
The chi-square analysis found a significant connection between uncomfortable workspace conditions and depression (χ2 = 7.997, p = 0.005). Individuals operating in uncomfortable workspace settings were more prone to experiencing heightened levels of depression compared to those in comfortable work environments (Table 7).
Table 7.
Association between workspace condition and depression
| Workspace condition | Comfortable (n = 198) |
Not Comfortable (n = 102) |
Total (n = 300) |
χ2 | p |
|---|---|---|---|---|---|
| Depression | |||||
| Depression | 11.1% | 23.5% | 15.3% | 7.997 | .005 |
Depression: Minimal depression (0–9); Depression (10–63)
Source: The researchers gather the data and applied the statistical software SPSS 25 to analyse the data, generating the necessary output for the study
Table 8 exhibits the outcomes of a Multivariable Logistic Regression examination exploring the correlation between depression and several predictor variables. Age, Marital Status, Working Hours, Police Humiliation, Workspace Distraction, Uncomfortable Workspace Conditions, Social Engagement, Job Satisfaction, and Supervisor Support were included as predictor variables in the analysis. The results indicate that Age (B = 1.117, Wald = 4.570, p = 0.033), Working Hours (B = 2.930, Wald = 26.555, p = 0.000), Police Humiliation (B = 0.988, Wald = 3.953, p = 0.047), Workspace Distraction (B = 2.817, Wald = 28.710, p < 0.001), Social Engagement (B = 1.628, Wald = 6.161, p = 0.013), Job Satisfaction (B = 1.813, Wald = 11.092, p = 0.001), and Supervisor Support (B = 1.156, Wald = 5.586, p = 0.018) were significantly linked to depression. To elaborate, for every one-unit increase in Age, there was a 3.055 times rise in the odds of experiencing depression. Conversely, for every one-unit increase in Working Hours, there was a 0.266 times decline in the odds of depression. Individuals reporting police humiliation were associated with 2.686 times higher odds of experiencing depression compared to those who did not report such experiences. Moreover, individuals experiencing higher levels of Workspace Distraction were linked to 16.728 times higher odds of experiencing depression compared to those with lower levels of distraction. Similarly, individuals dissatisfied with their job were linked to 6.129 times higher odds of experiencing depression compared to those who were satisfied. Furthermore, those who reported no support from their supervisor were linked to 3.177 times higher odds of experiencing depression compared to those who reported support (Table 8). However, Marital Status and Uncomfortable Workspace Conditions were not found to be significantly correlated with depression.
Table 8.
Multivariable logistic regression for depression and predictor variables
| Variables | B | S.E | Wald | df | Sig | Exp(B) | 95% C.I. for EXP(B) | |
|---|---|---|---|---|---|---|---|---|
| Lower | Upper | |||||||
| Age | 1.117 | .522 | 4.570 | 1 | .033 | 3.055 | 1.097 | 8.503 |
| Marital Status | .406 | .637 | .407 | 1 | .524 | 1.501 | .431 | 5.231 |
| Working Hours | 2.930 | .569 | 26.555 | 1 | .000 | 18.760 | 6.151 | 57.213 |
| Police Humiliation | .988 | .497 | 3.953 | 1 | .047 | 2.686 | 1.014 | 7.113 |
| Workspace distraction | 2.817 | .526 | 28.710 | 1 | .000 | 16.728 | 5.969 | 46.879 |
| Uncomfortable workspace conditions | .869 | .535 | 2.637 | 1 | .104 | 2.385 | .835 | 6.807 |
| No participation in social engagement | 1.628 | .656 | 6.161 | 1 | .013 | 5.093 | 1.408 | 18.420 |
| Not satisfied with job | 1.813 | .544 | 11.092 | 1 | .001 | 6.129 | 2.109 | 17.815 |
| No support from supervisor | 1.156 | .489 | 5.586 | 1 | .018 | 3.177 | 1.218 | 8.286 |
Discussion, conclusions and recommendations
The results of the study reveal significant associations between several factors and the prevalence of depression among the studied population. Age, working hours, police humiliation, workspace distraction, social engagement, job satisfaction, and supervisor support were found to be significantly associated with depression [18]. However, marital status and uncomfortable workspace conditions were not significantly associated with depression.
The study concludes that various factors significantly influence the likelihood of experiencing depression. Specifically, for every one-unit increase in age, there was 3.055 times increase in the odds of experiencing depression. Conversely, for every one-unit increase in working hours, there was 0.266 times decrease in the odds of depression. Individuals reporting police humiliation were associated with 2.686 times higher odds of experiencing depression compared to those who did not report such experiences [19]. Additionally, individuals experiencing higher levels of workspace distraction were associated with 16.728 times higher odds of experiencing depression compared to those with lower levels of distraction. Similarly, individuals not satisfied with their job were associated with 6.129 times higher odds of experiencing depression compared to those who were satisfied. Moreover, those who reported no support from their supervisor were associated with 3.177 times higher odds of experiencing depression compared to those who reported support. The results of related studies have illustrated that high job stress causes depression [20–22]. While various biological factors contribute to depression, the primary trigger often remains in the brain's stress adaptation system malfunctioning [23].
Research revealed that drivers who lacked a co-driver exhibited a particularly higher incidence of depression compared to those who had one. A co-driver serves as a valuable companion, providing companionship, support, and friendship during journeys.
Through a review of existing literature, it became evident that loneliness is a significant precursor to depression. However, engaging in intimate conversations with friends has been shown to alleviate depressive symptoms [23–25]. Sullivan defines loneliness as the discomfort stemming from insufficient intimate communication with others—a fundamental human need. When this need goes unmet, it can lead to feelings of isolation and, ultimately, depression [26].
According to study, receiving support from colleagues, employers, and friends can effectively mitigate job stress and enhance job satisfaction among workers [27]. Conversely, a dearth of such support can precipitate feelings of anger and depression, while simultaneously diminishing job satisfaction [28]. Conflict with others is identified as a potent stress inducer [28], but social support functions as a buffer against stress [29].
In the context of driving, interaction with various individuals along the route is typically shared between the driver and co-driver. Moreover, the presence of a co-driver can serve a supportive function during interpersonal conflicts, thus reducing stress related to social conflicts.
Recommendations
Based on the findings of the study, several recommendations can be made to help reduce the prevalence of depression among the studied population: As age was found to be significantly associated with depression, it is suggested that mental health services be made readily available and accessible to individuals as they age. Given the significant association between long working hours and depression, it is suggested that measures be taken to ensure that individuals do not work excessively long hours. This could include implementing strict regulations on working hours and ensuring that individuals have sufficient time for rest and relaxation.
The study found a significant association between police humiliation and depression. Therefore, it is recommended that measures be taken to prevent police humiliation and abuse. This could include providing training to police officers on how to interact with the public in a respectful and non-abusive manner.
Given the significant association between workspace distraction and depression, it is recommended that measures be taken to minimize distractions in the workspace. This could include providing individuals with a quiet and comfortable workspace.
The study found significant associations between job satisfaction, supervisor support, and depression. Therefore, it is suggested that measures be taken to improve job satisfaction and supervisor support. This could include implementing policies that promote a positive and supportive work environment.
Limitations of the study
During the course of our research, we encountered challenges in obtaining interviews with truck drivers. Many drivers were only available for short periods of time, such as when they stopped to take meals or rest, loading/unloading the goods. Furthermore, some drivers declined to participate in interviews altogether. As a result, our scheduled interview timeline was negatively impacted by these circumstances.
In this study most of the respondents were form the northern part of India. Those respondents who could easily understand the language were considered, which was one of the main reasons for exclusion of drivers from other part of the country.
Supplementary Information
Acknowledgements
We express our heartfelt appreciation to all the Indian truck drivers who graciously took part in our research, despite the heavy burdens and fatigue they endure. A special acknowledgment is owed to Mr. Kailash Prajapati for his invaluable contribution in developing the digital tool, Mr Phool Singh and Mr. Sujan Singh thoroughly contributed in data collection. Furthermore, we extend our sincere gratitude to the dedicated field investigators who meticulously gathered information from the truck drivers.
Ethical practice
We confirm that there are no potential conflicts of interest in relation to the research, authorship, and publication of this article. Prior to data collection, informed consent was obtained from all participants, and throughout the data collection process, privacy and confidentiality were strictly maintained, ensuring no ethical violations occurred.
Abbreviations
- IIHMR
Institute of Health Management Research
- NIMH
National Institute of Mental Health
- PPS
Probability Proportional to Size
- RR
Navigating the Road to Resilience
- SPSS
Statistical Package for the Social Sciences
- WHO
World Health Organization
Authors’ contributions
VB Tripathi was involved in the overall planning of the data collection, analysis, and ethical approval process. Snigdha Pareek contributed to data analysis, writing, and proper execution of paperwork for the paper.
Funding
The author has confirmed that they received no financial assistance for the research, authorship, or publication of this article.
Data availability
The datasets and materials used and/or analyzed during the current study are available from the corresponding author on reasonable request. All relevant data are included within the manuscript and its supplementary information files, ensuring transparency and reproducibility of the research findings.
Declarations
Ethics approval and consent to participate
This study was approved by the Institutional Review Board of IIHMR University, Jaipur, India under reference number IIHMR-U/IRB/2022/2. In addition, informed consent to participate in the interviews was obtained from all respondents.
Consent for publication
Not Applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets and materials used and/or analyzed during the current study are available from the corresponding author on reasonable request. All relevant data are included within the manuscript and its supplementary information files, ensuring transparency and reproducibility of the research findings.