Do Females and Males Experience Occupational Stress Differently? Insights From a Meta-Analysis

Abstract

Occupational stress has been extensively studied due to its key role in shaping the well-being, safety, and productivity of workers. However, existing studies offer conflicting evidence regarding the association between gender and occupational stress. This research systematically reviewed 18 studies with 20 records from the past decade on gender differences in occupational stress, utilizing meta-analysis techniques. Based on the literature review, occupational stress was categorized into physical, psychological, safety, and work stress. Moderators affecting the relationship between gender and occupational stress were analyzed. The findings suggested that female workers exhibited greater work stress (Cohen's d = –0.119, p < 0.001) and less safety stress (Cohen's d = 0.106, p = 0.010) than males. Region was a key moderator significantly affecting the relationship between gender and occupational stress (p < 0.05). These findings emphasize the distinct stress challenges faced by female and male workers, highlighting the need for tailored support to decrease occupational stress for workers of both genders, thus enhancing workers' performance, safety, and well-being.

1. Introduction

Occupational stress is a critical factor influencing individual well-being, safety, organizational performance, and socio-economic development [1]. High stress can compromise workers' safety by impairing their concentration and judgment and elevating the likelihood of accidents [2]. Eighty-three percent of American workers experience occupational stress, which is associated with an estimated 120,000 deaths each year [3]. In the United Kingdom, 964,000 workers reported illnesses in 2024 that were caused or worsened by occupational stress, representing about 52% of all self-reported illnesses [4]. Occupational stress accounted for 15% of the 16 reported health issues affecting Chinese workers across 23 industry sectors in 2022 [5]. In Australia, work stress accounted for 2,030 severe psychological stress claims in 2022, representing 25.2% of total mental health claims [6]. The Gallup World Poll reported that 35.1% of respondents experienced stress, with a higher prevalence among females (36.1%) than males (33.6%) [7]. A study of occupational stress and its determinants such as gender differences can help establish a healthy work environment, enhance workers' well-being, promote occupational safety, and improve workers' performance [8].

Historically, occupational stress has not been a singular concept but rather an integrated process involving the assessment, reaction, and response to stressors alongside efforts to achieve professional goals [9]. Lazarus and Folkman [10] conceptualized occupational stress as a dynamic association between workers and their surroundings, where their perceived needs exceed available resources. Therefore, this study defines occupational stress as a detrimental emotional and physical response when the work demands exceed a worker's abilities and desires [11]. According to previous studies, occupational stress can be generally categorized into four types: physical, psychological, safety, and work stress [12,13]. Physical stress arises when external demands surpass a worker's capacity to maintain physiological equilibrium [14,15]. Likewise, psychological stress emerges when workers face external pressures that overwhelm their ability to maintain emotional stability, leading to tension, fatigue, and imbalance [16]. Safety stress, on the other hand, is triggered by workplace safety hazards that pose risks to employee well-being [17]. Meanwhile, work stress occurs when job demands outstrip a worker's knowledge and competencies, leading to frustration and reduced effectiveness [18,19].

The association between gender and occupational stress has been previously explored. Mensah [20] showed that work stress adversely impacts the mental health of European workers, with females experiencing greater work stress than males. Viertiö and Kiviruusu [21] found that female workers in the Finnish workforce reported higher psychological stress than male workers. Shimura, Yokoi [22] found that male teleworkers reported lower psychological and physical stress than their female counterparts. Desai and D'souza [23] found that female workers in the construction industry were more prone to work-related health issues and experienced greater safety pressures than male workers. Kerr, Da Torre [24] surveyed workers at Canadian psychiatric hospitals and discovered that female workers experienced more physical stress but less work stress than male workers. Monsey, Beckman [25], and Kuusi, Tervo-Niemelä [26] discovered that factors such as occupation type and age influence gender differences in stress levels. Although several studies have explored the association between gender and occupational stress, the inconsistent results from previous studies highlighted the need for a thorough investigation of how gender influences workers' occupational stress and its various forms.

Previous studies have reviewed and analyzed the association between gender and occupational stress. Oakman, Kinsman [27] performed a meta-analysis of research on gender differences in occupational stress from 2007 to 2020. They discovered that female workers reported higher exhaustion and stress than male workers when working at home. Salin [28] reviewed 30 articles on workplace bullying and stress, discovering that some studies reported no gender differences in stress levels, while others indicated more intense stress symptoms in female workers than male workers. However, existing review studies do not address the following key research questions. (1) What is the statistical significance and overall magnitude of gender differences in physical, psychological, safety, and work stress when aggregated across studies? and (2) What are the key moderators influencing gender differences in occupational stress? Therefore, a meta-analysis review was performed in this study to synthesize existing research, answer the above-mentioned questions, and offer thorough insights into the association between gender and occupational stress over the past decade. The findings can inform efforts to reduce occupational stress for female and male workers and offer a solid theoretical foundation for future research on enhancing workers' well-being, safety, and productivity.

2. Method

2.1. Literature search

The literature was collected through searches of Web of Science, PubMed, Google Scholar, ProQuest, Scopus, and Science Direct over the last 10 years (2015−2024) to capture recent research developments and current research trends. The keywords for all searches were (worker OR employee OR personnel) AND (stress OR occupational stress OR work stress OR job stress OR psychological stress OR physical stress OR safety stress) AND (gender differences OR women OR men OR female OR male OR sex differences). Study selection, data extraction, and quality assessment were independently conducted by two researchers. Discrepancies were resolved through discussion, with a third researcher adjudicating unresolved issues. Two thousand one hundred and seventy-seven studies were retrieved in the initial screening phase. Studies were screened according to the following inclusion criteria. First, all participants in the included studies were workers active in the labor market. Second, this study included only peer-reviewed journal articles published in English, excluding conference abstracts and dissertations [29]. Third, the included studies must compare stress levels between male and female workers. Fourth, the test data (e.g., Cohen's d, male and female worker sample sizes) in the included studies must be complete [30]. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guideline was used for this review [31], ensuring methodological rigor and standardized reporting. Fig. 1 illustrates the screening process. After reading the abstracts and keywords, 85 articles satisfied the inclusion criteria. Sixty-seven articles were excluded for lacking information on the relationship between gender and occupational stress or for insufficient data to calculate the target effect sizes. First, eleven studies were excluded because their samples were not restricted to workers active in the labor market. For example, Corrente and Park [32] included individuals who were absent from work or business and adolescents. Costa and Briguglio [33] focused on unemployed individuals rather than currently employed workers. Larsen and Lasgaard [34] drew on a general population sample that included retirees and individuals outside the workforce. Maeda and Nomura [35] incorporated a substantial proportion of full-time homemakers. Proctor and Hopkins [36] included farm owners' spouses, generally categorized as dependents. Second, 15 studies were excluded because they were not peer-reviewed journal articles published in English. For instance, Farah [37] was published only as a conference abstract, while Ahumada, Ansoleaga [38], Ansoleaga, Díaz [39], Bauer, Bendels [40], Beschoner, Braun [41], and Oliveira, Gomes [42] were not published as English-language articles. Third, twenty-five studies did not provide a direct comparison of occupational stress levels between male and female workers. Representative examples included Böckelmann, Zavgorodnij [43], which focused on burnout arising from work stress rather than occupational stress; Chung and Kim [44], which treated gender solely as a control variable; Lee and Choi [45], which included only male participants; Pajovic and Shuey [46], which identified stress patterns using latent class analysis without directly comparing mean stress levels by gender; and Vigna and Brunani [47], which examined associations between stress and metabolic syndrome without reporting direct gender-based stress comparisons. Finally, 16 studies were excluded due to insufficient statistical data for meta-analytic synthesis. For example, Capecchi and Di Iorio [48] presented gender differences only in bar charts without numerical values. De Sio and Cedrone [49] reported medians and quartiles instead of means and standard deviations. Kim and Park [50] provided only regression coefficients. Li and Sun [51] reported rank means rather than parametric statistics. Tsubono and Oba [52] reported Mann–Whitney U test results without effect size estimates such as Cohen's d. These exclusions ensured methodological consistency and comparability across the studies included in the meta-analysis. These examples demonstrated that the exclusions were based solely on objective methodological requirements (e.g., insufficient statistical reporting, absence of extractable effect-size data, or ineligible study design), rather than selective study inclusion. The procedure follows established best-practice guidelines for meta-analysis and ensures the robustness, reproducibility, and interpretability of the synthesis [53]. To ensure transparency and methodological rigor, the studies excluded for failing to meet the predefined inclusion criteria have been explicitly summarized in Table 1. Finally, 18 peer-reviewed journal articles were selected and examined. A second round of literature searching and verification was conducted to confirm that no eligible studies were overlooked. Numerous studies have indeed quantitatively measured and reported occupational stress. However, comparatively fewer investigations have focused specifically on occupational stress within the targeted gender-differences analytical scope of this study. For instance, Kavosi, Ranaei, and Kordshouli [54] conducted a meta-analysis encompassing 63 studies on occupational stress. However, only 13 of these studies addressed gender-related dimensions. Milner and Witt [55] performed a meta-analysis of 22 studies examining the association between occupational stress and suicidal tendencies. Rauschenbach and Krumm [56] conducted a meta-analysis investigating age-related indicators of occupational stress, incorporating 19 peer-reviewed studies. In contrast, this study synthesized evidence from 18 high-quality investigations specifically examining gender differences. This approach aligns with established scholarly conventions in the field while satisfying methodological requirements for meta-analysis, particularly with respect to sample representativeness and the rigorous integration of empirical findings [53].

Fig. 1.

PRISMA flowchart depicting the screening process. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

Table 1.

Articles excluded for failing to meet the inclusion criteria

Exclusion criteria	Full-text articles excluded
No worker samples (11)	Backhaus, Hoven [57], Corrente, Park [32], Costa, Briguglio [33], Larsen, Lasgaard [34], Maeda, Nomura [35], Noh, Kim [58], Olsen, Øverland [59], Proctor, Hopkins [36], Temmen and Crockett [60], Yan, Zhong [61], Yu, Manku [62]
No English peer-reviewed journal articles (15)	Ahumada, Ansoleaga [38], Ansoleaga, Díaz [39], Bauer, Bendels [40], Beschoner, Braun [41], Campos, Angélico [63], Chen, Liu [64], Fang, Jiang [65], Farah [37], Gravante, Toscano [66], Guedes and Gaspar [67], Kacem, El Maalel [68], Oliveira, Gomes [42], Pogosova, Isakova [69], Ramos and Jordão [70], Wiegel [71]
No stress gender comparison (25)	Alenezi, Gillespie [72], Apaydin, Rose [73], Böckelmann, Zavgorodnij [43], Carmassi, Dell'Oste [74], Chung, Kim [44], Damm and Monn [75], Deguchi, Iwasaki [76], Diekmann, Böckelmann [77], Gascon-Santos, Oliván–Blázquez [78], Gold, Kuznia [79], Graham, Aleksa [80], Håkansson and Ahlborg Jr [81], Koura, Sekine [82], Lee, Choi [45], Lian, Qi [83], Islam, Ekuni [84], Kim, Shin [85], Pajovic and Shuey [46], Piantella, McDonald [86], Sharma and Srivastava [87], Skotnicki, Wilson [88], Vigna, Brunani [47], Violanti, Fekedulegn [89], Wacker, Fischer [90], Yıldırım, Dilekçi [91]
No sufficient data for testing analysis (16)	Asamoah–Appiah and Aggrey–Fynn [92], Capecchi, Di Iorio [48], Chin and Tat [93], De Sio, Cedrone [49], Heub, Schaller [94], Kim, Park [50], Li, Sun [51], Méndez–Rivero, Pozo [95], Miller, Reichert [96], Osca and López–Araújo [97], Pahn and Yang [98], Pike, Wadsworth [99], Pu, Zhou [100], Ratanasiripong, Bell [101], Tashiro, Kato [102], Tsubono, Oba [52]

2.2. Coding for studies

Author information, measurement, sample size, region, publication year, mean age, and occupation type were gathered from each paper. Effect sizes (male-to-female) were computed and transformed into Cohen's d [103]. For studies with multiple subgroups (e.g., measurement, mean age, region, or occupation type), effect sizes were computed and documented individually for each subgroup. Twenty outcomes were retrieved from the 18 articles. The quality of the included studies was assessed using the Newcastle–Ottawa Scale (NOS), a standardized tool for evaluating nonrandomized studies. The NOS evaluates three domains: selection, comparability, and outcome (or exposure), across eight items with a maximum score of nine. Study quality was categorized as low (≤4), moderate (5–6), and high (≥7). All included studies were rated as high quality.

According to the included studies, occupational stress measurements were categorized into four types to facilitate the meta-analysis of the articles: physical, psychological, safety, and work stress. Moderating variables including worker age, occupation type, and region, were coded to disaggregate the data and examine the effect of demographic factors on gender differences in occupational stress. Regions were categorized into five types including Oceania, East and Central Asia, South and Southeast Asia, Europe, and North America. Four of the collected articles focused on healthcare workers, while three focused on academic staff. All other occupation types were represented by either a single article or lacked disclosure. Therefore, workers' occupation types were categorized as healthcare workers, academic staff, and others. The specific coding for potential moderators is provided in Table 2.

Table 2.

Coding details for potential moderators

Variable	Category	Coding method
Measurement	Physical stress, Psychological stress, Safety stress, Work stress	•Physical stress: physical strain of work, chronic stress (allostatic load) •Psychological stress: mental strain of work, psychological distress •Safety stress: safety-related stress •Work stress: work strain, workplace stress
Occupation type	Healthcare worker, Academic staff, Others	•Healthcare worker: healthcare provider, clinician, psychological practitioner •Academic staff: academic faculty, academic librarian, academic healthcare worker •Others: auditor, teleworker, church worker
Mean age	Mean age, NA	Mean age of all respondents
Region	Oceania, East and Central Asia, South and Southeast Asia, Europe, North America, NA	•Oceania: New Zealand •East and Central Asia: China •South and Southeast Asia: Indonesia •Europe: Finland •North America: United States, Canada

Note: NA = Not available.

2.3. Meta-analysis

The meta-analysis was conducted with a random-effects model using Comprehensive Meta-Analysis (Version 3.0) software. The distribution of observed effect sizes was analyzed using a random-effects model, accounting for variations across studies in participants and measurement methods. Heterogeneity refers to the variation in true effect sizes across studies and can influence the complexity of drawing overall conclusions [104]. This study quantified heterogeneity using Q statistics and I² [105]. The Q value is influenced by the number of studies [106], whereas the I² value is unrelated to the effect size magnitude and study count [107]. Therefore, I² was chosen to complement the Q statistic in quantifying heterogeneity. If heterogeneity exists, meta-regression will be conducted to explore moderating variables and sources of heterogeneity. Publication bias occurs when certain findings such as positive results are more prone to be published, while negative findings often remain unpublished [108]. Additionally, small studies are less prone to be published unless the results are statistically significant [109]. Begg's and Egger's tests were utilized to evaluate publication bias [110]. Egger's test, which is more reliable for small samples than Begg's test, is frequently used with funnel plots and the fail-safe N to detect publication bias [111]. The classical fail-safe N, introduced by Rosenthal [112], assesses the robustness of aggregated effects by estimating the number of missing studies required to nullify the overall effect. This study utilized the trim-and-fill method to adjust for potential publication bias [113].

3. Result

3.1. Overall effect size

A random-effects model analysis of 20 records retrieved from 18 articles produced an aggregated effect size of –0.096 (95% confidence interval [CI] = [–0.140, –0.053], p < 0.001), comparing occupational stress levels between male and female workers (Table 3). The aggregated effect size indicated that female workers experience higher occupational stress than male workers. When categorized by the measurements of occupational stress, the aggregated effect sizes for physical, psychological, safety, and work stress were –0.105 (95% C.I. = [–0.282, 0.071], p = 0.243), –0.011 (95% C.I. = [–0.141, 0.119], p = 0.870), 0.106 (95% C.I. = [0.025, 0.186], p = 0.010), and –0.119 (95% C.I. = [–0.164, –0.074], p < 0.001), respectively (Fig. 2). The findings revealed that female workers had greater work stress and less safety stress than male workers. The levels of physical and psychological stress among female workers were not significantly different from those of male workers.

Table 3.

Random effects meta-analysis of the association between gender and occupational stress

Effect size and 95% C.I.					Test of null (2-tailed)
Number of records	Point estimate	Standard error	Variance	95% C.I.	Z-value	p-value
20	–0.096	0.022	0.001	[–0.140, –0.053]	–4.330	<0.001

Fig. 2.

Forest plot of pooled studies categorized by measurements employing a mixed-effects analysis. Note: The center of a circle indicates the estimated Cohen's d value for the study. The circle's area corresponds to the study's weight in the meta-analysis, and the horizontal line's width represents the confidence interval. The center of the diamond is the overall Cohen's d, and its width represents the confidence interval.

3.2. Test of heterogeneity

The results of the random-effects meta-analysis for the heterogeneity test are shown in Table 4. Q and I² values are indicators of the heterogeneity levels. Higgins and Thompson [114] proposed a classification of heterogeneity with I² of 75%, 50%, and 25% representing high, medium, and low, respectively. Table 4 shows high heterogeneity in the overall records (Q = 108.53, I² = 82.49%).

Table 4.

Results of the heterogeneity test

Heterogeneity
Q-value	df (Q)	p-value	I2 (%)
108.53	19	<0.001	82.49

3.3. Test and adjustment for publication bias

This study assessed publication bias using Egger's regression intercept test [115] and Begg and Mazumdar's rank correlation test [116]. The Egger's regression intercept test (intercept = –0.524, 2-tailed p = 0.535) and Begg and Mazumdar rank correlation test (Kendall's tau b = –0.147, 2-tailed p = 0.364) in this study showed no support of publication bias.

The classic fail-safe N in this study was 506, significantly surpassing the threshold of 5n + 10 = 110 (with n representing the number of studies in the meta-analysis). This outcome demonstrated that the influence of unpublished studies on the overall effect size is minimal, affirming the robustness of the overall effect observed.

Duval and Tweedie [117]'s trim-and-fill method was employed to acquire the estimate of the unbiased effect size. In the funnel plot comparing differences in occupational stress between female and male workers, two extra data points were inserted in the right of the mean. The random-effects-based total effect size changed from –0.096 (95% C.I. = [–0.140, –0.053]) to –0.092 (95% C.I. = [–0.136, –0.049]) (Fig. 3). When the trim-and-fill method was applied, the 15 observations of work stress measurements were adjusted by adding two data points to the right of the mean. The random-effects-based total effect size changed from –0.119 (95% C.I. = [–0.164, –0.074]) to –0.115 (95% C.I. = [–0.160, –0.070]) (Fig. 4). The trim-and-fill method was not implemented for the physical, psychological, and safety stress measurement records due to fewer than five records being available, the minimum required for this method [118]. After adjusting for publication bias, the standardized mean difference in occupational stress levels between female and male workers was –0.092. Specifically, female workers reported higher work stress and lower safety stress than male workers, with a larger effect size in work stress than in safety stress. The gender differences in physical and psychological stress levels were not significant.

Fig. 3.

Funnel plot of the total records adjusted for publication bias. Note: Open circles represent original data, and solid circles represent imputed filled values. The observed point estimate is represented by the open diamond at –0.096 (95% C.I. = [–0.140, –0.053]), and the imputed point estimate is represented by the solid diamond at –0.092 (95% C.I. = [–0.136, –0.049]).

Fig. 4.

Funnel plot of the work stress measurement records adjusted for publication bias. Note: Open circles represent original data, and solid circles represent imputed filled values. The observed point estimate is represented by the open diamond at –0.119 (95% C.I. = [–0.164, –0.074]), and the imputed point estimate is represented by the solid diamond at –0.115 (95% C.I. = [–0.160, –0.070]).

3.4. Moderator analysis

The heterogeneity test results confirmed the presence of heterogeneity among the included studies. This study employed meta-regression analyses to explore possible sources of heterogeneity in overall effect sizes through individual covariates. According to the included studies, potential moderators include region, mean age, and occupation type. Mean age was a continuous variable, while occupation type and region were categorical variables represented using dummy coding. Table 5 provides the outcomes of single-covariate meta-regressions conducted with maximum likelihood methods and random effects across all records. The coefficient of a categorical variable reflects the marginal effect, measured as the standardized mean difference, when the reference category is subtracted from the focal category [119]. Table 5 indicates that region (p < 0.05) significantly moderated the overall effect size. However, occupation type (p > 0.05) and mean age (p > 0.05) showed no significant impact on the total effect size. The corresponding R² for region was 0.52, meaning that 52% of the variance in true effects can be explained by the covariate. If the region changes from Europe to East and Central Asia, the overall effect sizes would increase by 0.177. There were no significant differences in total effect sizes for North America, Oceania, or South and Southeast Asia, compared to Europe.

Table 5.

Outcomes of moderator analyses conducted through meta-regression

Covariate	Coefficient	Z-value	2-tailed p-value	Test to model	R2
Occupation type (reference to healthcare worker)
Intercept	–0.046	–0.70	0.485	/	0.20
Academic staff	–0.166	–1.47	0.140	Q = 2.32, df = 2, p = 0.313
Others	–0.090	–0.97	0.330
Mean age
Intercept	0.123	0.78	0.434	/	0.26
Mean age	–0.004	–1.02	0.309	Z = –1.02; p = 0.309
Region (reference to Europe)
Intercept	–0.071	–1.63	0.102	/	0.52
East and Central Asia	0.177	2.00	0.046	Q = 10.96, df = 4, p = 0.027
North America	–0.063	–1.23	0.218
Oceania	0.062	0.69	0.490
South and Southeast Asia	–0.108	–0.88	0.378

Note: Q = the sum of squared deviations on a standardized scale; Z = the Z-value for a test of the null (that the true coefficient is zero); p = 2-tailed p-value indicating statistical significance for each level of the model; and R² = the proportion of variance explained by the predictors.

4. Discussion

4.1. Relationship between gender and occupational stress

This meta-analysis study revealed that female workers had a higher work stress level than male workers. Female workers are more affected by the lack of social support in the workplace, especially from supervisors, when experiencing stress compared to male workers [120]. From a socialization perspective, female workers are often expected to assume more emotional and caregiving roles than male workers, which heightens females' work stress levels when support is lacking [121,122]. Brown and Moloney [123] demonstrated that female workers encounter more challenging work environments than male workers including frequent job interruptions, inequitable pay, and mismatches between job demands and individual skills. Due to insufficient recovery time and the cumulative psychological and physical strain of high-intensity work, female workers experience greater work stress than male workers [124]. Furthermore, female workers are more prone than male workers to experiencing work stress because of conflicting work and family responsibilities [125]. Male workers often view work as an important aspect of their self-identity, deriving fulfillment, and reducing stress through professional achievements [126]. In contrast, female workers often shoulder primary responsibilities for family and childcare [127]. The trivialities of life usually make it difficult for females to balance family and work, leading to increased stress [128]. When examining why female employees often experience higher work stress than their male counterparts, it is crucial to consider patriarchal culture and various forms of workplace harassment including sexual and nonsexual incidents. Females are more likely to encounter behaviors ranging from workplace harassment to overt discrimination than males, which can significantly contribute to chronic work stress [129]. Females statistically face high rates of inappropriate comments, unwanted advances, and boundary violations in professional settings [130]. Even when harassment is not overt, past exposure can create a lasting emotional burden that undermines females' sense of safety and belonging [131]. Additionally, patriarchal culture can reinforce unequal power structures in the workplace [132]. Leadership and authority are still often implicitly associated with masculinity, requiring females to navigate stereotypes such as being viewed as overly emotional [133]. Under patriarchal workplace norms, females face barriers such as the “broken rung” effect, where they are less likely than males to receive their first promotion [134]. Therefore, females must work harder to outperform male colleagues to gain equal recognition, which increases work stress [135]. Furthermore, the glass ceiling effect creates invisible limits that prevent females from reaching senior or executive roles despite equal or greater qualifications [136]. These layered obstacles mean females not only experience more scrutiny and fewer opportunities for recognition and support but also carry a higher emotional and psychological load, resulting in greater work stress compared to males [137].

However, female workers had a lower safety stress level than male workers. Male workers are more likely than female workers to undertake excessive physical labor, leading to fatigue, diminished attention, and reduced reaction time, which increased accident risk, and greater safety stress [138]. Moreover, male workers are more likely than female workers to rely on experience or intuition over safety training and protocols due to empirical and cultural influences, leading to accidents and increased safety stress [139,140]. Zhou and Xu [141] found that safety stress was significantly higher for males than females among healthcare workers. This finding may be attributed to the high proportion of males working in physician roles, particularly in high-risk departments with great exposure to patients or potential sources of infection, which increases safety stress for male workers [142].

The differences in psychological and physical stress between male and female workers were not significant, possibly due to variations in measurement methods across studies, differences in how psychological and physical stress are perceived by male and female workers, and the varying degrees of concern participants had regarding these stressors [143]. The findings underscored the necessity for further studies to explore and analyze the differences in psychological and physical stress between male and female workers.

4.2. Moderating variables

4.2.1. Occupation type

Previous individual studies investigated the role of occupation type in the relationship between gender and occupational stress. For instance, Mensah [20] showed that service sector workers experience a higher work stress level than those in the industrial sector. This disparity is primarily due to the greater emotional labor, frequent high-intensity customer interactions, and unstable work schedules characteristic of the service industry, which contribute to increased work stress for workers, especially women. Kuusi and Tervo-Niemelä [26] examined psychological stress levels among diaconal workers, church musicians, pastors, and Christian youth workers and discovered greater gender differences in psychological stress among church musicians compared to other occupation types. These findings may be attributed to the irregular working hours and demanding workload typical of male church musicians. The results of this study were different from previous studies and revealed no significant effect of occupation type on gender differences in occupational stress. The nonsignificant result in this study may be attributed to the small number of included studies that specified participants' occupation types, leading to considerable controversy in the literature [144].

4.2.2. Mean age

This study revealed that participants' mean age was not a significant moderator affecting the total effect size. This result suggested that gender differences in occupational stress may exist across all worker populations irrespective of age. The need for targeted stress reduction measures was highlighted for male and female workers of all ages. Additionally, Monsey and Beckman [25] found that female workers typically reported higher occupational stress than male workers, with the gap being more pronounced among young workers than among old workers. This finding may be due to young female workers shouldering great dual responsibilities in family and work, particularly during the early stages of their careers or during childcare responsibilities. Elliott and Blithe [145] found that when female workers gained experience and resources with age, their occupational stress levels in nonacademic institutions decreased and aligned closely with those of male workers. The findings suggested that career stability helps female workers manage and reduce occupational stress. These inconsistencies underscore the need for further research on the relationship between age and gender differences in occupational stress.

4.2.3. Region

Region significantly influenced the overall effect size of gender differences in occupational stress. This result aligned with previous research on occupational stress. Drummond and O'Driscoll [146] discovered that female workers in collectivistic cultures (e.g., China), which emphasize family support, tend to rely more on supportive resources than those in individualistic cultures (e.g., Australia and New Zealand). However, female workers in collectivistic cultures receive less social support than their male counterparts, resulting in pronounced gender disparities in occupational stress levels. According to Curzi and Pistoresi [124] and Mensah [20], gender differences in occupational stress are more pronounced in Southern Europe compared to Northern Europe. Northern Europe may exhibit reduced gender differences in occupational stress due to greater social support and flexible work policies, whereas traditional gender roles in Southern Europe may amplify gender differences.

This study revealed that gender differences in occupational stress were more significant in East and Central Asia than in Europe. This phenomenon may stem from cultural differences in emotional expression norms and social expectations [147]. In East and Central Asian cultures, male workers often express stress as a symbol of work effort or responsibility. At the same time, societal expectations of female resilience discourage female workers from seeking psychological support, leading to accumulated occupational stress and widening gender disparities [148]. In contrast, European cultures prioritize mental health awareness and encourage individuals to express stress and seek help [149]. European workers, especially females, often have access to mental health support, maintain clear work-life boundaries, achieve work-family balance, and experience reduced work stress [150].

4.3. Theoretical and practical implications

4.3.1. Theoretical implications

This study identified notable methodological shortcomings in prior research on gender differences in occupational stress. First, many studies lacked longitudinal surveys, limiting the ability to capture the long-term occupational stress levels of workers. Second, some studies, such as Ross and Sagrera [151] with 14 male participants and Patel and Ghebre [152] with 31 male participants, were constrained by limited sample sizes. Future research on workers' occupational stress should incorporate large samples to enhance statistical reliability in subsequent studies. Finally, the meta-analysis revealed that the region factor moderates gender differences in occupational stress. This study incorporated a limited amount of literature on workers' regions. Future research should explore gender disparities in occupational stress across underrepresented regions (e.g., Africa and Oceania).

4.3.2. Practical implications

This research revealed that female workers experience significantly higher work stress than male workers, underscoring the need to focus on reducing work stress for female workers. First, organizations should offer regular health assessments and implement targeted support programs for female workers such as stress reduction workshops and health seminars [153]. These initiatives provide essential emotional support and help alleviate work stress, particularly for female workers. Second, organizations should support female workers in developing leadership and management skills through training programs and job rotation initiatives [137]. These approaches empower female workers with a voice and decision-making authority, enhance their sense of control at work, and reduce work stress. Finally, organizations should promote an inclusive culture that encourages female workers to overcome gender stereotypes, thereby reducing female workers' psychological burden and work stress associated with gender roles [154].

This research also indicated that female workers experience significantly lower safety stress than male workers, underscoring the need to focus on reducing safety stress for male workers. First, organizations should offer comprehensive safety training programs to enhance male workers' safety behaviors, thereby reducing safety risks and alleviating safety stress among male workers [119]. Second, organizations should ensure that workers are provided with suitable personal protective equipment [155]. This approach can effectively reduce male workers' anxiety related to safety concerns, thereby minimizing safety stress. Finally, organizations should strengthen the safety culture by implementing initiatives such as regular safety education weeks [156]. The strengthened safety culture ensures that workers, especially males, understand and value safety measures, reducing safety stress at work.

4.4. Limitations

Although this research provided valuable insights into gender differences in occupational stress, several limitations need to be considered when drawing conclusions from the findings. First, this research was limited by the inclusion of only 18 studies because incomplete reporting in previous research prevented the extraction of effect sizes from certain relevant studies. Research on occupational stress in sectors such as apparel manufacturing, mining, shipping, supply chain industries, and plantations was excluded from the meta-analysis. These studies relied solely on qualitative designs or lacked comparable quantitative data such as extractable statistical results. Furthermore, many of the included studies focused on healthcare workers, academics, and other professional groups, which do not fully represent core industrial workers, limiting the generalizability of the findings to labor-intensive or manufacturing-based occupations. Future research should include representative samples from industrial sectors and ensure comprehensive reporting of effect sizes in studies on gender differences in occupational stress including detailed results regardless of significance. Second, the included studies exhibited variations in their categorizations of occupational stress, with most focusing on specific aspects rather than providing a comprehensive evaluation of occupational stress. Future research on gender differences in occupational stress should adopt a comprehensive definition of occupational stress and develop standardized classifications to encompass its various dimensions.

5. Conclusion

This research performed a meta-analysis of 20 records on occupational stress, derived from 18 articles published over the past decade. Standardized mean differences in occupational stress levels between male and female workers from the records were synthesized using meta-analysis. Although the results indicated that female workers experienced higher overall occupational stress levels than male workers, female workers exhibited greater work stress and less safety stress than males. Additionally, region was significantly associated with gender differences in occupational stress. Gender differences in occupational stress were more significant in East and Central Asia than in Europe. These findings can provide the basis for initiatives to reduce occupational stress among female and male workers, thereby enhancing workers' well-being, safety, and workforce productivity.

CRediT authorship contribution statement

Siu Shing Man: Writing – review & editing, Writing – original draft, Supervision, Methodology, Funding acquisition, Formal analysis, Conceptualization. Yizhen Fang: Writing – review & editing, Writing – original draft, Investigation, Funding acquisition, Data curation, Conceptualization. Yingwei Chen: Writing – review & editing, Writing – original draft. Li Liu: Writing – review & editing, Writing – original draft, Methodology, Funding acquisition, Formal analysis, Conceptualization. Alan Hoi Shou Chan: Writing – review & editing, Supervision, Resources, Conceptualization.

Data availability

Data will be made available on request.

Statement on the use of AI

The authors declare that they did not use generative artificial intelligence (AI) and AI-assisted technologies in the writing process.

Funding

This research was supported by the National Natural Science Foundation of China (72301110), the Philosophy and Social Science Planning Project of Guangdong Province of China (GD25YYS35), the China Postdoctoral Science Fund (2024M760318), the Yangcheng Youth Scholar Project of Philosophy and Social Science Planning of Guangzhou (2025GZQN07), the 1st Batch of 2024 MOE of PRC Industry-University Collaborative Education Program (230805329290236) and the Fundamental Research Funds for the Central Universities (QNMS202418).

Conflicts of interest

All authors have no conflicts of interest to declare.