Introduction

The textile and garment industry in Vietnam holds a significant economic position, with export revenues reaching approximately $40 billion in 2023, accounting for about 11.3% of the country's total exports. This sector provides employment to about 2.7 million workers, representing a substantial portion of the national workforce, and plays a crucial role in poverty reduction and socio-economic development [1]. However, garment industry workers are exposed to various health issues. Musculoskeletal disorders are reported as the most frequent health problem, along with cardiovascular diseases, neurological issues, and mental health problems, all affecting both physical and mental well-being [2,3]. The causes of these health problems include environmental factors within the factory, such as dust, noise, heat, and lighting, as well as ergonomic factors related to posture during work. Additionally, social factors like poor management practices and low wages, and psychological pressures such as the stress of meeting daily targets and the fear of job loss, also have a significant impact on workers' health, ultimately quality of life (QoL) [3,4]. The working conditions in Vietnam’s garment factories are also poor, with personal protective measures often being minimal [5]. Excessive working hours and frequent overtime are common, with many workers working 50 to 60 hours per week, far exceeding the legal standard of 40 hours. Although Vietnamese labor law allows only 30 hours of overtime per month, approximately 70% of factories were reported to have exceeded this limit [6].

The QoL of workers is an important indicator for assessing an individual's health status and overall QoL, and it plays a crucial role in enhancing productivity [7]. It is influenced by various factors, including demographic characteristics, work-related factors, health-related behaviors, and psychosocial factors. Previous studies have shown that work-related factors such as working hours [8] and night shifts [9] are associated with workers' QoL, in addition to demographic factors like educational level. Health-related issues such as poor sleep quality and behaviors like physical activity are also linked to QoL [10]. Depression negatively affects workers' psychological well-being, reducing their QoL and causing difficulties in daily life and social functioning [11]. Job stress has been found to increase depression and anxiety while lowering job satisfaction, ultimately leading to a negative impact on QoL[6]. Self-efficacy, a positive cognitive function individuals have about themselves, helps them effectively cope with their environment, ultimately enhance the QoL of workers [12]. Social support, perceived as the availability of social resources provided by supervisors or colleagues, has a positive impact on workers' QoL [13].

Although the importance of the garment industry in Vietnam is growing, there is still a lack of research focused on the QoL of factory workers in this sector. Therefore, this study aims to assess the level of QoL among Vietnamese garment factory workers and to understand the factors that may influence it using a quantile regression. Quantile regression analysis is employed to offer insights beyond the average effects typically provided by traditional methods, highlighting how factors impact Vietnamese garment factory workers at different QoL levels. This approach allows for more targeted and effective interventions tailored to specific needs within the QoL spectrum [14]. The influencing factors include demographic and socio-economic characteristics, working conditions, health related factors, depression, job control, self-efficacy, and social support. The findings of this study are expected to serve as foundational data for the development of programs and policies aimed at improving the QoL of garment factory workers in Vietnam. Ultimately, their improved QoL is anticipated to contribute to global economic development and public health promotion.

Methods

Study population

The target population of this study consists of workers employed in garment factories in Vietnam, while the accessible population includes local workers employed in seven Vietnamese garment factories (four in Ho Chi Minh City, two in the Mekong Delta, and one in the Central region). The factories ranged from small-scale apparel manufacturers employing 1,500 to 4,000 workers to large-scale facilities producing for global brands, with workforces of up to 100,000. Some specialize in sports and leather shoe production, while others focus on a diverse array of apparel, including outerwear, activewear, and children’s wear. This diversity highlights the presence of both vertically integrated mega-factories and smaller, niche garment manufacturers within Vietnam’s textile and apparel industry.

The inclusion criteria for participants are: (1) adults aged 18 years or older, (2) employed in a garment factory for at least three months, and (3) understanding the purpose of this study and voluntarily agreeing to participate. The exclusion criteria include workers who have migrated from other countries, those who have been employed for less than six months, individuals with serious health conditions, or those who have previously participated in similar surveys.

The sampling method employed in this study was convenience sampling, a non-probability approach. To determine the minimum required sample size, a statistical power analysis was conducted using the G*Power 3.1.9.2 program. Based on a significance level of .05, an effect size of .15, a desired statistical power of .95, and 29 predictor variables, the minimum required sample size was calculated as 256 participants. For the present study, an initial total of 300 participants were recruited. Approximately 10% of participants were excluded from the analysis due to insincere or incomplete responses, such as consistently selecting the same answer across all items or leaving numerous pages blank. For the remaining data, cases with missing values were handled using listwise deletion to ensure consistency in the multivariate analyses. The final sample size consisted of 270 participants. The sample size of 270 participants in this study exceeds the minimum requirement for ordinary linear regression as determined by G*Power and is considered appropriate for quantile regression analysis based on prior simulation-based recommendations [15]. Notably, the sample size represents approximately 0.01% of the Vietnamese garment industry workforce in 2023.

Recruitment and data collection procedures

The survey was administered at seven garment factories in Vietnam located in Ho Chi Minh City (4 factories), the Mekong Delta (2 factories), and the Central region (1 factory), with factory sizes ranging from 1,000 to 56,000 employees. The purpose of the study was explained to the factory representatives, and permission was obtained to recruit participants. Recruitment notices were posted on each factory’s bulletin board to invite workers to participate. A local research assistant in Vietnam visited the factories to meet the workers in person, explained the study objectives, and informed them of their rights as research participants before obtaining written consent. The survey was conducted during the workers' lunch breaks or on their way home after work to minimize disruption to their schedules. Questionnaires were distributed in person and collected on the spot to ensure a high response rate. Each worker took approximately 30 to 60 minutes to complete the questionnaire, which was longer than in the pilot study due to differences in age and levels of understanding. As a token of appreciation for their participation, hand sanitizers and masks were provided to the participants. The survey was conducted from March 1 to June 14, 2021, with rigorous compliance to COVID-19 safety measures, including social distancing, mask wearing, and hand hygiene. The study approved by the Institutional Review Board (IRB) of the Jeonbuk National University (IRB No: 2020-11-007).

Measurements

Sociodemographic variables

Sociodemographic variables included gender, age, marital status, education level, monthly income, and place of residence.

Work- and workplace-related variables

Work- and workplace-related variables were measured using self-reported items. Participants were asked to report the total number of years they had been employed as well as their tenure at the current factory. They also selected their primary work department from a list of options including sewing, packing and finishing, quality control, ironing, cutting, cleaning, mobile work, buttoning, screen printing, and laundry. Working posture was assessed by asking whether their typical work posture was sitting, standing, or walking. Perceived workload was categorized as light, medium, or heavy based on participants’ subjective assessment. Daily working hours were recorded as a continuous variable. Shift work status was determined by asking whether they worked during the day, evening, or night shifts. Absenteeism was assessed by asking whether they had been absent from work due to sickness in the past month (1~2days/ more than 2days /no). Perception of the work environment was measured by asking participants to rate their working conditions as either good or bad, and the adequacy of safety protection was assessed based on whether workers felt they had access to sufficient protective materials (adequate/inadequate). Job satisfaction and experience of occupational accidents were both measured with yes/no questions.

Health- and health behavior-related variables

Health- and health behavior-related variables were also collected. Participants were asked to identify whether they experienced any health problems by checking all applicable items from a list including chronic pain, dysuria, musculoskeletal symptoms, menstrual disorder, gastric ulcer, fatigue, headache, and others. Frequency of exercise per week was measured by asking whether they exercised never, 1–3 days, 4–6 days, or every day. Subjective general health status was assessed using a five-point scale (excellent, very good, good, fair, poor). Smoking status was determined by asking whether the participant was currently smoking (yes/no), and alcohol consumption was measured by asking about the frequency of drinking with response options ranging from never to more than four times a week. Self-reported height and weight were used to calculate body mass index (BMI). Dietary behaviors were measured by asking whether participants had eaten breakfast on the day of and the day before the survey (yes/no), and by asking about the number of times per day they consumed fruits and vegetables (none, 1–2 times, or more than 3 times). Sleep-related variables included average sleep duration (in hours) and perceived sleep quality, which was rated on a five-point Likert scale (very good, good, neither poor nor good, poor, very poor).

Quality of life

QoL was measured using the WHOQOL-BREF, developed by the World Health Organization [16]. This tool consists of 26 items. The items are rated on a 5-point Likert scale, ranging from 1 ("Not at all") to 5 ("Very much"), with higher scores indicating better QoL. The reliability of the tool (Cronbach's α values) at the time of development ranged from .66 to .82 across the domains, and .87 in the current study.

Depression

Depression was measured using the CES-D (Center for Epidemiological Studies Depression) scale developed by Radloff [17]. This tool consists of 20 items rated on a 4-point Likert scale ranging from 0 ("Rarely or none of the time") to 3 ("Most or all of the time"), with higher scores indicating more severe depressive symptoms. The reliability of the CES-D showed a Cronbach's α of .90 [18], and .93 in the current study.

Job control

Job control was measured using the Job Content Questionnaire (JCQ), developed by Karasek et al. [19]. The JCQ includes 27 items measuring psychological demands (job demands), decision latitude (job control), and social support (support from colleagues and supervisors). For this study, only the job control section was used. Higher scores of job control indicate a greater degree of autonomy and control over work tasks. The English and Vietnamese versions of the JCQ were purchased from the JCQ Center Global (https://www.jcqcenter.com/usage-request-information/). At the time of development, the tool demonstrated acceptable internal consistency, with Cronbach’s α values of .73 for women and .74 for men [19], and .84 in the current study.

Self-efficacy

Self-efficacy was measured using the General Self-Efficacy Scale developed by Schwarzer and Jerusalem [20]. This tool, consisting of 10 items, assesses an individual’s confidence in coping with behaviors necessary for maintaining health. It is rated on a 4-point Likert scale ranging from 1 ("Not at all true") to 4 ("Exactly true"), with higher scores indicating higher self-efficacy. The reliability of the tool at the time of development showed a Cronbach's α of .76, and .86 in the current study.

Social support

The Multidimensional Scale of Perceived Social Support (MSPSS) developed by Zimet et al. [21] was used to measure social support. This instrument is freely available for download and use from the website (https://gzimet.wixsite.com/mspss). The MSPSS consists of three subscales: family support, friend support, and significant other support, with four items for each subscale, totaling 12 items. Each item is rated on a 5-point Likert scale, ranging from 1 ("very strongly disagree") to 5 ("very strongly agree"). The total score ranges from 12 to 60, with higher scores indicating greater perceived social support. In the original study by Zimet et al. [21], the Cronbach’s α coefficients for the subscales were .87 (family), .85 (friends), and .91 (significant other), with an overall reliability of .88 for the total scale, and .91 in the present study.

Translation of the questionnaire

The tools used in this study were translated into Vietnamese using the back-translation technique [22] after obtaining permission from the original developers. The process involved translating the original English version into Vietnamese by a professional bilingual expert fluent in both Vietnamese and English. This was followed by a back-translation into English by another independent bilingual expert who had not been exposed to the original questionnaire to ensure conceptual equivalence and translation accuracy. The two language versions were compared, after which the Vietnamese research assistant and the research team discussed and refined the translation to produce the initial version in the local language. A pilot survey was subsequently conducted with 20 factory workers in Vietnam to assess the clarity of the items and the time required to complete the survey. There were no difficulties in understanding the items, and the average time required to complete the survey was approximately 25 minutes.

Statistical analysis

In this study, quantile regression analysis was employed to examine the factors influencing the QoL of Vietnamese garment factory workers at the 10th, 25th, 50th, 75th, and 90th percentiles. This approach provides a comprehensive assessment of QoL across its distribution, capturing both central tendencies and variations in the lower and upper extremes. Unlike traditional regression methods, quantile regression effectively manages heteroscedasticity without assuming homoscedasticity, providing a deeper and more nuanced understanding of the data [23].

By enabling a systematic evaluation of QoL and its determinants, this analysis supports the development of targeted strategies to enhance QoL and highlights the groups requiring more intensive interventions. The assumptions of quantile regression—linearity, independence, and absence of multicollinearity—were satisfied. Multicollinearity was assessed using the Variance Inflation Factor (VIF). The VIF values for most variables ranged from 1.26 to 7.01 (Table 1), indicating no significant multicollinearity concerns. Slightly higher VIF values were observed for two dummy variables: middle school (VIF = 6.41) and high school (VIF = 7.01). However, these values remained below the commonly accepted threshold of VIF < 10, as supported by prior statistical literature [24]. The analysis was conducted using StataMP 17.0 version (StataCorp, 2021, College Station, TX). Sensitivity analysis using bootstrapping with 1,000 replications showed slight variations in the coefficient estimates compared to the original quantile regression results. However, the overall trends remained consistent, which supports the robustness of the findings

Results

Characteristics of study participants

The average age of the study participants was 29.5 years. Among the 270 participants, 30.4% were male and 69.6% were female. The majority of the participants had at least a high school education. Forty percent of the participants lived in rented housing, and more than half (64.1%) had a monthly income between $200 and $299. The most common work department was sewing (40.4%), and about one-third of the participants worked in shifts. While the majority (86.7%) were satisfied with their jobs, 17.0% perceived their working conditions as poor and felt that protective equipment was inadequate, with an accident rate of 15.9%. The smoking rate was 21.9%, while 41.9% of participants reported not engaging in any physical activity. About one-fifth of the participants reported sleeping less than six hours per night, and one in three rated their sleep quality as average. Half of the participants reported having health problems, and approximately 27.8% perceived their health status as average or poor (Table 2).

Descriptive statistics for main study variables

The mean score for QoL was 66.7 with a standard deviation (SD) of 7.2, ranging from 37.5 to 85.7. Social support had a mean of 62.1 (SD=9.8), with scores ranging from 21.0 to 84.0. Depression showed a mean of 12.1 (SD=11.6), with a range of 0.0 to 50.0. Job control had a mean of 24.7 (SD=3.2), with scores between 13.0 and 36.0. Self-efficacy showed a mean of 28.6 (SD=5.1), with a range of 14.0 to 40.0.

Differences in QoL according to the characteristics of study participants

The differences in QoL according to the characteristics of the study participants are presented in Table 2. There was no significant difference in QoL scores between males (66.23) and females (66.84). However, statistically significant differences were observed in education level (F=3.12, p=.016) and income level (F=4.70, p=.003). Differences in QoL according to the work-related characteristics of the participants were also observed. Statistically significant differences were found in the duration of employment at the garment factory (t=25.44, p<001), working section (F=2.64, p=.017), working posture (F=6.42, p=.002), workload (F=4.61, p=.011), shift work (t=8.64, p=.004), absenteeism (F=3.35, p=.037), working condition (t=27.08, p<.001), protection materials (t=44.21, p<.001), and satisfaction with job (t=13.79, p<.001). Differences in QoL according to the health behaviors of the participants were also observed. Statistically significant differences were found in drinking (F=4.86, p=.001), physical activity (F=5.99, p=.001), breakfast (t=23.43, p<.001), daily fruit or vegetable intake (F=3.52, p=.031), sleep quality (F=13.19, p<.001) and health problem (t=24.10, p<.001).

The correlation analysis results showed that QoL had a strong positive correlation with self-efficacy (r=.51, p<.001), job control (r=.49, p<.001), and social support (r=.85, p<.001). Conversely, there was a significant negative correlation between QoL and depressive symptoms (r=-.33, p<.001).

Quantile regression analysis of QoL

This study utilized quantile regression analysis to understand the various factors influencing the QoL of Vietnamese garment factory workers. The impact of education on QoL varied across different quantiles (Table 1). Higher education levels, such as university/college education, showed a positive coefficient at the lower 0.25 quantile (B=3.22, p=.042), indicating better QoL compared to primary education at lower quantiles. Regarding monthly income, those earning 200-299 USD and above 400 USD showed significant impacts at the lower 0.1 quantile (200-299 USD: B=2.83, p<.001; above 400 USD: B=3.03, p=.024). In addition, those earning above 400 USD showed significant impact at the 0.75 quantile (B=3.79, p=.049). Shift work was associated with higher QoL at the lower 0.1 quantile (B=1.14, p=.029), but showed a negative impact at the upper 0.75 quantile (B=-2.47, p=.001) and 0.9 quantile (B=-2.43, p=.002). "Neither good nor poor" sleep quality had a significant negative impact on QoL across all quantiles compared to "good" sleep quality. The effect was most pronounced at the 0.1 quantile (B=-4.13, p<.001) and continued to have substantial negative effects across other quantiles (0.25 quantile: B=-3.53, p<.001; 0.5 quantile: B=-3.09, p<.001; 0.75 quantile: B=-2.14, p=.004; 0.9 quantile: B=-1.64, p=.032).

At the 0.9 quantile, specific work sections within the garment factory significantly impacted QoL. Workers in quality control showed a substantial negative impact on QoL (B=-4.90, p<.001), as did those in ironing (B=-3.86, p=.015) and screen printing (B=-3.49, p=.023). Conversely, a walking posture was associated with better QoL (B=2.14, p=.047). In the case of fruits vegetables intake, both consuming 1~2 times per day (B=2.49, p=.024) and more than 3 times per day (B=2.65, p=.026) per day were associated with better QoL. At the 0.75 quantile, a medium workload was associated with worse QoL compared to a light workload (B=-1.59, p=.038) and absenteeism of more than 2days per month had a significant impact on QoL (B=2.28, p=.048) at the 0.1 quantile.

Depression had a negative impact on QoL at the 0.25 quantile (B=-0.06, p=.023) and showed the strongest negative impact at the 0.9 quantile (B=-0.10, p=.002). Self-efficacy had a significant impact at the 0.1 quantile (B=0.12, p=.021) and also showed significant effects at the 0.75 quantile (B=0.17, p=.025), indicating a stronger influence on QoL as the quantile increased. Job control positively influenced QoL at the 0.1 quantile (B=0.31, p<.001), 0.25 quantile (B=0.27, p=.010), and 0.5 quantile (B=0.29, p=.010). Social support had the most substantial positive impact on QoL across all quantiles, showing significant effects at the 0.1 quantile (B=1.21, p<.001), 0.25 quantile (B=1.35, p<.001), 0.5 quantile (B=1.42, p<.001), 0.75 quantile (B=1.51, p<.001), and 0.9 quantile (B=1.49, p<.001). (Table 1, Supplementary Figure 1)

Discussion

The effects observed across different quantiles explain the diverse impacts of socioeconomic status, working conditions, and psychosocial factors on the QoL of Vietnamese garment factory workers.

Being compared with other countries in Asia, the Vietnamese findings (average QoL = 66/100) align with patterns observed among garment workers in other Asian countries, with similar social and occupational factors at play. Studies across Bangladesh, India, China, and Cambodia consistently show that poor mental health and exhausting work conditions erode QoL, while education and support networks act as protective buffers. For example, Bangladeshi and Indian garment workers often endure intense physical strain (e.g. chronic back pain, headaches) and high psychosocial stress under harsh factory conditions, contributing to lower QoL [25,26].

QoL among workers in this study is influenced by socioeconomic status, including education and income levels. Previous studies, education appears important too – in settings where garment workers have relatively higher education and better awareness of health and rights (for instance, Sri Lanka), workers tend to experience better health outcomes and QoL [27]. However, in this study, while education significantly impacted QoL at the 0.25 quantile, it did not have a significant effect at the 0.9 quantile. This may be because individuals with relatively low QoL perceive education as providing opportunities for better jobs, enhancing social status, and ensuring economic stability through graduation from university. On the other hand, individuals who already have high QoL, may not be significantly influenced by their education level, as they are already economically stable. Regarding income, it was found to have a positive impact on QoL in the 0.1 and 0.75 quantiles. Park and Kim [28] also identified income as a significant factor affecting workers' QoL. This could be because, for lower-income groups, income plays a crucial role in maintaining basic living standards, while for middle-income groups, it acts as a factor in improving the QoL. Therefore, to enhance the QoL of garment factory workers, it is necessary to consider ways to guarantee a basic living wage or implement a reward system that compensates workers based on their performance.

Shift work positively influenced QoL at the 0.1 quantile but negatively impacted QoL at the 0.75 and 0.9 quantiles. Typically, as shown in the study by Lim et al. [9], shift work is known to have a negative impact on QoL. Participants at the 0.1 quantile may have found shift work to provide opportunities for additional financial incentives such as night shift premiums or higher hourly wages, leading to economic stability, and allowing for more flexible time management [29]. For employees who are struggling or have low baseline QoL, the extra income and job security gained by working shifts may relieve some stressors and enhance certain aspects of life quality. Conversely, shift work may have negatively affected QoL among groups with higher QoL due to its irregular nature, potential work-family conflict, which disrupts sleep patterns and increases job stress [30]. The findings of this study demonstrate that the impact of shift work on QoL varies according to workers’ baseline condition and life priorities, thereby underscoring the necessity for tailored interventions. Rather than evaluating shift work as uniformly beneficial or detrimental, it is essential to design and implement policies that are differentiated based on the QoL. Compensation systems prioritizing economic stability such as night shift premiums for low-QoL groups, and flexible shift policies such as self-scheduled rotations for high-QoL groups to mitigate disruptions. These implications suggest a direction for inclusive and effective labor welfare policies and call for a paradigm shift in labor policy that places greater emphasis on the quality of work.

Consumption of fruits and vegetables was associated with QoL in the 0.9 quantile, suggesting that a nutritious diet can enhance both physical and mental health. A previous study involving 3,000 industrial employees also reported a significant positive association between dietary quality and QoL [31]. This relationship can be explained through biological, psychological, and social mechanisms: fruits and vegetables reduce disease risk through anti-inflammatory and antioxidant actions [32], and are linked to improved mental health, mood, and cognitive well-being [33]. A healthier diet also enhances energy, daily functioning, and the ability to participate in social activities by reducing fatigue and increasing vitality [34]. Therefore, the findings of this study suggest that interventions targeting health related behaviors may be effective in enhancing overall well-being and provide a basis for the development of preventive health strategies and welfare policies aimed at improving QoL. Accordingly, workplace interventions such as providing fruits and vegetables during snack times and offering financial support for healthy food options are recommended. The statistically non-significant association between fruit and vegetable consumption and QoL in low-QoL groups may reflect the dominance of competing variables such as income level and absenteeism could overshadow the marginal benefits of dietary improvements.

Sleep quality was found to have a strongly negative impact on QoL across all quantiles when compared to "good" sleep quality, especially among those who reported "neither good nor poor" sleep. This result highlights the significant influence of sleep quality on the QoL of factory workers and suggests that managing sleep quality should be a top priority in efforts to improve workers' QoL. Sleep disturbances are common as well – about one-fifth of Bangladeshi garment workers report insomnia, reflecting the toll of long working hours and stress on well-being [35]. A study on manufacturing workers in Malaysia also emphasized that poor sleep quality, particularly related to night shifts, mediates the relationship between working conditions and reduced QoL [9]. This indicates that improving workers' sleep quality can bring substantial benefits to their physical and mental well-being, thereby enhancing QoL, suggesting the need for policies such as sleep education, adequate rest, and nap times.

The findings highlight the importance of improving specific working conditions for participants with higher levels of QoL, particularly at the 0.9 quantile. QoL was significantly lower in quality control, ironing, and screen printing compared to sewing. Sewing typically involves lower physical demands and provides a more comfortable working environment compared to the high temperatures and repetitive motions associated with ironing and screen printing.

The results of this study indicate that depression has a negative impact on QoL at the 0.25 and 0.9 quantiles. These results are significant in that they demonstrate depression has a widespread impact across both low and high QoL groups. Fundamentally, these findings are consistent with previous research showing that depression reduces QoL among manual laborers [36,37]. Depression among factory workers can diminish emotional well-being, impair social functioning, and further decrease QoL [38]. In groups with low QoL, depressive symptoms may affect basic life satisfaction and overall functional living. In contrast, among those with high QoL, although their overall life quality appears to be good, the psychological burden of depression may still be significant. This indicates that depression is closely related not only to living conditions or economic status but also to internal psychological states, highlighting that psychological health management is essential for improving QoL. Therefore, targeted interventions—such as routine depression screening, psychosocial support programs, and counseling—are recommended to reduce depression and enhance QoL. A workplace health promotion program for manufacturing workers in Malaysia, which reported reduced stress, anxiety, and depression scores and improved QoL [39], it is evident that similar programs should be developed to reduce depression and enhance QoL among Vietnamese workers.

Self-efficacy was identified as a positive factor influencing QoL at the 0.1 and 0.75 quantiles. In lower QoL groups (0.1 quantile), self-efficacy may function as a protective buffer, enabling individuals to cope with life challenges such as disease-related stress and limited resources. Therefore, interventions that support basic psychological stability and the improvement of daily functioning, such as providing small success experiences and relaxation techniques, are essential. In upper-mid QoL groups (0.75 quantile), self-efficacy serves as a facilitator that helps optimize well-being and supports the pursuit of higher-level goal setting [40]. Therefore, in this group, interventions focused on self-actualization and achievement are effective. Workers with high self-efficacy possess strong problem-solving skills and the ability to effectively cope with job stress. Strategies such as self-development programs, goal-setting training, and providing positive feedback should be implemented to enhance self-efficacy, leading to improved job performance and, ultimately, enhanced QoL.

Job control was found to positively influence QoL in the lower quantile groups (0.1, 0.25, 0.5). This has been consistently associated with improved well-being and QoL among workers [41]. Interventions aimed at enhancing job control such as increasing workers’ participation in decision-making and providing opportunities for skill development may help improve QoL, particularly among groups with lower QoL.

Social support emerged as a very strong positive factor influencing QoL across all quantiles. This finding aligns with the study by Xiao et al. [42]. In Bangladesh, workers who feel supported by supervisors or peers report significantly better self-rated health too [26]. When workers receive support from colleagues or supervisors, they experience emotional stability and a positive ability to cope with stressful situations. High levels of social support also enhance their sense of belonging and connection, enabling them to recover from difficulties, which plays a crucial role in improving their overall QoL. Social support can be provided through various social networks, including friends and colleagues. Vietnam’s collectivist culture and community ties may provide a support network that buffers stress, consistent with the observed boost in QoL from social support, whereas in Bangladesh many young women migrate from rural villages to city factories and live away from family, a separation that adds to stress and depression burdens [43].Therefore, it is necessary to introduce programs that promote mutual support and cooperation, such as regular workshops, social clubs, and efforts to foster a culture of mutual respect and collaboration among workers within the workplace.

Strengths and limitations

The strengths of this study include its use of quantile regression analysis to identify factors affecting QoL at different quantiles (0.1, 0.25, 0.5, 0.75, 0.9), considering a wide range of factors, such as education, income, shift work, and dietary habits, resulting in comprehensive findings. However, the study has limitations. Self-reported data may introduce bias, and caution is needed when generalizing the findings due to the relatively small sample size compared to the larger population of garment workers in Vietnam. It did not deeply analyze interactions between factors, potentially limiting the understanding of how these factors collectively influence QoL. Moreover, the cross-sectional nature of the data limits the ability to assess changes over time or long-term effects, suggesting a need for further research.

Conclusion

This study examined the factors influencing the QoL among Vietnamese garment factory workers using quantile regression analysis. It is recommended to implement programs that aim to mitigate the negative effects of shift work and support healthy eating habits among those in the higher QoL group. In addition, among workers in the higher QoL group, targeted monitoring and tailored support for those in departments such as quality control and ironing may be warranted. The results also indicate that depression had a negative impact on QoL 0.25 and 0.9 quantiles, suggesting the need for tailored mental health support for workers at both lower and higher ends of the QoL spectrum. Sleep quality and self-efficacy were positively associated with QoL, indicating the potential benefits of interventions focusing on sleep programs and personal empowerment. Job control showed a positive association with QoL at lower quantiles, which may reflect the motivational aspects of manageable stress in some subgroups. Social support consistently influenced QoL across all quantiles, highlighting the importance of fostering supportive workplace environments. These findings suggest that multilevel strategies addressing mental health, workplace support, and individual coping resources may contribute to improving QoL among garment factory workers in Vietnam.

Supplementary Materials

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