Preplanned Studies: Associations of Occupational Stress and Coping Styles with Well-Being Among Couriers — Three Cities, Zhejiang Province, China, 2021

In recent years, the rapid development of the express industry in China has led to an increase in occupational stress among couriers, potentially contributing to poor well-being (1). A positive attitude in the workplace significantly impacts reducing occupational stress, and coping styles are crucial factors in well-being (2-3). This study aimed to investigate the association between occupational stress, coping styles, and well-being and explore the intermediary effect of coping styles on the relationship between occupational stress and well-being among couriers. The research used a cluster random sampling method to select 1,200 couriers from mainstream express companies in three cities (Ningbo, Jiaxing, and Taizhou) within Zhejiang Province, China. The Core Occupational Stress Scale (COSS) was employed to assess occupational stress, the World Health Organization’s five-item Well-Being Index (WHO-5) scale to measure well-being, and the Trait Coping Style Questionnaire (TCSQ) to evaluate coping styles. Poor well-being was found to be relatively common among couriers in the three cities within Zhejiang Province, accounting for 43.5% in this study. Couriers’ well-being was correlated with occupational stress (r_s=−0.142, P<0.01) and positive coping styles (r_s=0.059, P<0.05), with occupational stress exhibiting a direct effect on well-being and an indirect effect through positive coping styles. The findings of this study emphasize the need for a coordinated, multi-level effort to control the prevalence of occupational stress among couriers and actively guide them in managing stressors to promote their mental health.

This cross-sectional study was conducted from September to November 2021 and included 1,200 employees aged 18 years or older, with at least one year of work experience from 20 leading express companies in Ningbo, Jiaxing, and Taizhou cities in Zhejiang Province. An anonymous, self-administered questionnaire was employed for data collection, and all investigators underwent uniform training prior to conducting the survey. The study received approval from the Medical Ethics Committee of the National Institute of Occupational Health and Poison Control, and informed consent was provided by all participants. A total of 1,161 valid questionnaires were collected, yielding a response rate of 96.8%.

The WHO-5 scale was employed to assess subjective well-being (4), with a total score below 13 indicating poor well-being. Occupational stress was evaluated using the COSS (5), which consists of four subscales: social support, organization and return, demand and pay, and autonomy. A total score exceeding 50 denotes occupational stress. The TCSQ, comprising 20 items, is divided into positive coping and negative coping aspects, each containing 10 items. The scores for both coping styles are determined by summing up their respective items. Data analysis was conducted using SPSS (version 25.0, IBM Corporation, Armonk, NY, USA) and AMOS (version 24.0, IBM Corporation, Armonk, NY, USA). The relationship between occupational stress, coping styles, and well-being was examined via Spearman’s rank correlation analysis. To further investigate the associations among occupational stress, coping styles, and well-being, structural equation modeling (SEM) was implemented. Two-tailed P values <0.05 were deemed statistically significant.

The study sample included 1,161 couriers, primarily aged between 31 and 40 years and with 1–5 years of work experience (Table 1). The prevalence of occupational stress among these couriers was found to be 52.9%. A majority (72.5%) of the couriers exhibited a predominantly positive coping style, whereas 19.6% displayed a negative coping style. Poor well-being was reported by 43.5% of the participants.

A significant difference in the prevalence of poor well-being was observed between couriers with different education levels (χ²=10.932, P=0.027). Additionally, a statistically significant difference was found in the prevalence of poor well-being among couriers with various working hours (χ²=23.416, P<0.001).

Table 2 presents the results of the correlation analysis between occupational stress, coping styles, and well-being. Couriers’ well-being was found to be negatively correlated with occupational stress (r_s=−0.142, P<0.01) and positively correlated with the positive coping style (r_s=0.059, P<0.05). However, no significant correlation was observed between well-being and negative coping style (r_s=0.011, P>0.05).

The final output model illustrated research variables’ correlations and effect paths (Figure 1). It can be observed that occupational stress was negatively related to the positive coping style (β=−0.107, P<0.05), and the positive coping style was positively related to well-being (β=0.010, P<0.001).The 95% confidence interval (CI) of the estimation of the two-mediation path does not include 0, which means that the total effect of occupational stress on well-being was statistically significant, and occupational stress had significant indirect effects through the positive coping style on well-being (Supplementary Table S1). However, the negative coping style did not demonstrate direct or indirect effects on well-being (P>0.05).

Figure 1. 

Standardized path analysis illustrating the relationships among occupational stress, coping styles, and well-being among couriers in three cities in Zhejiang Province, China, 2021.

Note: The three cities included in the study were Ningbo, Jiaxing, and Taizhou. The fit indices for the structural model were as follows: χ²=37.576; df=11; χ²/df=3.416; P<0.001; CFI=0.981, GFI=0.991, AGFI=0.976; NFI=0.986; RMSEA=0.046. A model is considered to have a good fit if it meets the following criteria: RMSEA<0.08, CFI>0.90, and NFI>0.90. Based on these results, the structural model demonstrated a good fit with the data in this study.

Abbreviation: CFI=comparative fit index; GFI=goodness-of-fit index; AGFI=adjusted goodness-of-fit index; NFI=normed fit index; RMSEA=root-mean-square error of approximation.

* P<0.05.

** P<0.001.