Next Article in Journal
Development of a Light Duct Using Relay Lenses and Diffusers
Previous Article in Journal
Exploring Nonlinear Effects of Visual Elements on Perceived Landscape Quality in Historical and Cultural Districts: A Deep Learning Case from Wuhan, China
Previous Article in Special Issue
Correlation Between Construction Typology and Accident Rate—Case Study: Balearic Islands (Spain)
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Buildings
Volume 15
Issue 23
10.3390/buildings15234339
buildings-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Impact of Pandemic-Induced Psychosocial Hazards on the Mental Health Outcomes of Construction Professionals

by
Shang Zhang
1,
Riza Yosia Sunindijo
2,*,
Carol K. H. Hon
3,
Haoxiang Li
1,
Zhenwen Su
4 and
Peng Kang
1,5
1
Department of Construction Management, Suzhou University of Science and Technology, Suzhou 215009, China
2
School of Built Environment, UNSW Sydney, Sydney, NSW 2052, Australia
3
School of Architecture and Built Environment, Queensland University of Technology, Brisbane, QLD 4001, Australia
4
Tianping College, Suzhou University of Science and Technology, Suzhou 215009, China
5
Suzhou No. Two Construction Group Co., Ltd., Suzhou 215131, China
*
Author to whom correspondence should be addressed.
Buildings 2025, 15(23), 4339; https://doi.org/10.3390/buildings15234339 (registering DOI)
Submission received: 18 September 2025 / Revised: 28 October 2025 / Accepted: 25 November 2025 / Published: 28 November 2025
(This article belongs to the Special Issue Advances in Safety and Health at Work in Building Construction)
Browse Figures
Versions Notes

Abstract

The outbreak of the COVID-19 pandemic and the control measures implemented by governments have caused serious harm to the physical and mental health of many, including professionals working in the construction industry. As one of the largest industries globally, it is important to examine the psychosocial hazards that occurred during the pandemic and their impacts on the mental health outcomes of construction professionals, especially those working for construction enterprises, as their work tends to be site-based and has been influenced more seriously during the pandemic. This research used a questionnaire to gather data from 531 professionals working in China’s construction industry. Descriptive analysis, Spearman’s correlation analysis, and partial least-squares structural equation modeling (PLS-SEM) were employed to analyze the data. The findings indicate that work–family-related factors were critical psychosocial hazards. Their prevalence rates of depression, anxiety, and stress symptoms are 32.96%, 32.58%, and 16.95%, respectively. Personal factors are the strongest predictors of poor mental health outcomes, especially anxiety, while work–family-related factors are influential on the development of depression symptoms. The findings are unique, as they reveal critical psychosocial hazards that affect the mental health of construction professionals during the pandemic. They provide important theoretical references for governments or construction enterprises to develop disaster management plans in cases of similar public health emergencies in the future.

1. Introduction

Mental ill-health is a serious issue in the construction industry. For instance, one-third of construction workers in the UK experience high levels of anxiety, and their suicide rate is 3.7 times higher than the national average [1]. In America, 29.6% of male construction workers suffer from psychological distress, worsened by illegal drug use and alcohol abuse, which increases their likelihood of suicidal ideation [2]. Nwaogu et al. [3] also revealed that 55% and 14.4% of construction supervisors in Nigeria have depression and anxiety symptoms, respectively.
The COVID-19 pandemic has had a profound adverse economic, social, environmental, and health impact globally [4,5,6]. Social isolation, harsh government measures, and remote working policies have worsened mental health problems, doubling the number of anxiety and depression cases in some countries [7,8]. Other pandemic-induced psychosocial hazards include fear of contagion; stigmatization, especially toward infected people and healthcare workers; telework-related hazards; rapid digitalization demand; job insecurity; violence at work and home; and work–life imbalance [9]. Another study found that dealing with contradictory instructions, lack of means to carry out work while following restrictions, having to show good mood and empathy, and having to do things against personal beliefs were also additional psychosocial hazards during the pandemic [10].
The construction industry was seriously affected by the pandemic and the government’s corresponding strict measures [11]. In China’s construction industry, poor mental health due to industry-specific psychosocial hazards, such as long work hours, high work demands, and work–life imbalance, has been recognized in the literature [12]. Measures to prevent the spread of COVID-19, such as travel restrictions, close contact tracing, mass testing, isolation directives, centralized quarantine programs, and social distancing [13], acted as additional psychosocial hazards and prevented construction projects from operating normally. The pandemic control measures increased construction costs and disrupted the supply of labor and material [14]. Those working on sites, a typical characteristic of working in the construction industry, faced even more challenging work environments than those working in offices, where restrictions imposed by the government can be more readily implemented [15]. Construction was previously dubbed an “epicenter” of infectious diseases, which has exacerbated skilled labor shortage and increased project costs by 40 and 20%, respectively [16]. All of these pandemic-induced psychosocial hazards further worsened construction workers’ mental health [6].
To improve mental health in the construction industry, it is important to identify psychosocial hazards, comprising psychological, social, cultural, and environmental factors, and their adverse influence on workers’ behavior and health [17]. Using a meta-analysis, Sun et al. [18] found that role conflict has the strongest relationship with mental health problems, followed by job insecurity, role ambiguity, and interpersonal conflict. In another study, Sun et al. [19] applied the job demands–resources (JD-R) model to examine key psychosocial hazards affecting mental health, including poor physical work environment, contract demands, and lack of support from colleagues. Interviewing 33 construction practitioners in Australia, Hon et al. [20] revealed 18 psychosocial hazards. Although these studies examined the psychosocial hazards in the construction industry, the one-size-fits-all approach is not suitable for managing the mental ill-health of Chinese construction professionals, specifically during the pandemic, because psychosocial hazards are context-specific and shaped by socioeconomic and cultural differences [3,21]. In addition, the identified factors in existing research mainly focus on job-related factors in the non-pandemic period, which do not fully cover the unique psychosocial settings caused by the pandemic.
The mental health of construction professionals in China has received little attention despite the tremendous size of the industry and its significant impact on national and international socioeconomic developments [12]. In 2023, for instance, the Chinese construction industry employed more than 50.43 million people [22], and its total output value was more than RMB 32.65 trillion [23]. Furthermore, previous studies have overlooked research on psychosocial hazards and their impacts on the mental health outcomes of construction professionals during the COVID-19 pandemic period. In this research, therefore, these two problems have been integrated to investigate the pandemic-induced psychosocial hazards experienced by professionals working in the Chinese construction industry and the impact of those hazards on their mental health outcomes. This is significant because these professionals manage site-based activities, which were prone to experiencing disruptions and specific psychosocial hazards due to the pandemic. Furthermore, identifying pandemic-induced psychosocial hazards and their influence on mental health is important to improving the readiness of the construction industry in facing future emergencies and protecting the well-being of construction professionals. This effort ensures the resilience of the construction industry, an important industry that has a considerable influence on the socioeconomic stability and development of a nation. To achieve this aim, the objectives of the research are to (1) identify the critical pandemic-induced psychosocial hazards among construction professionals in China; (2) evaluate the mental health conditions of these professionals during the pandemic; and (3) explore the impacts of pandemic-induced psychosocial hazards on their mental health outcomes during the pandemic.

2. Literature Review and Conceptual Model Development

2.1. Psychosocial Hazards

The transactional theory [24] proposes two groups of psychosocial hazards, comprising environmental and personal factors. According to this theory, mental health is influenced by both environmental stressors and personal traits. Pirzadeh et al. [25] also found that the work environment is a critical factor leading to mental illness in professionals in the construction industry. Liu et al. [26] assessed the influence of environmental and socioeconomic factors on people’s mental health conditions during the pandemic, and they found that increased greenspace exposure is related to better mental health outcomes. In China, work–family conflict is the top stressor causing construction professionals’ poor mental health [12]. Likewise, a previous systematic review found home–work conflict to be a top stressor in the construction industry [27]. Frimpong et al. [28] categorized 30 psychosocial hazards into personal, socioeconomic, and organizational or industrial dimensions. The literature review results indicate the important association of psychosocial hazards with personal factors [24,28], work–family-related factors [12,27], and environmental factors [24,25]. Underpinned by the transactional theory and built on extant research findings on psychosocial hazards and mental health during the pandemic, Zhang et al. [29] identified 20 pandemic-induced psychosocial hazardous factors, which were classified into three dimensions, including personal, work–family, and environmental factors. The details of these hazardous factors are shown in Table 1.

2.2. Mental Health

As reported earlier, construction professionals have poorer mental health primarily due to an adverse work environment, a demanding workload, and long work hours [12,75]. According to a systematic review [76], the three prominent types of mental illnesses in construction are depression, anxiety, and stress. These three mental illnesses were also used to measure the mental health conditions of construction professionals in this research. The pandemic has further worsened the mental health of the general population, including those working in the construction industry. Nochaiwong et al. [40] revealed that the global prevalence for mental health conditions was 28.0% for depression, 26.9% for anxiety, and 36.5% for stress during the pandemic. Data collected from 5070 Australian adults indicated that 78% of them suffered from deteriorating mental health conditions compared with the beginning of the pandemic. In addition, over half of the participants reported that they experienced increased levels of psychological distress, depression, anxiety, and stress due to loneliness, uncertainty about the future, and financial concerns [77]. Khan et al. [78] found that the lockdowns, social distancing policies, and reduced workforce increased stress among construction employees. However, existing research is silent on the mental health conditions of construction professionals during the pandemic.

2.3. Conceptual Model Development

A conceptual model was developed, as shown in Figure 1. First, this study assessed the criticality level of pandemic-induced psychosocial hazards, comprising personal factors, work–family-related factors, and environmental factors (see Table 1), faced by construction professionals in China. This step identified the critical psychosocial hazards that they experienced during the pandemic. Second, this research evaluated the levels of depression, anxiety, and stress of these professionals during the pandemic, which indicate their mental health conditions. Third, this research investigated the impact of the pandemic-induced psychosocial hazards on the mental health outcomes of construction professionals. Understanding the impact is critical to develop strategies to reduce the negative influence of these hazards and improve mental well-being in the construction industry in case similar public health emergencies occur in the future.

3. Materials and Methods

3.1. Data Collection

Achieving the three research objectives necessitated the collection of numerical data from a large number of respondents so that levels of psychosocial hazards and mental health outcomes, and the relationship between them, could be assessed quantitatively. Adopting the quantitative methodology, this research used a questionnaire to collect data from construction professionals in China. The questionnaire comprised three main sections. The first section collected the demographic data of construction professionals, including their age, gender, and type of company (see Table 2 for details). The second section employed the Depression, Anxiety, and Stress Scales-21 (DASS-21) [79] to assess the respondents’ mental health conditions. DASS-21 is a widely used instrument to measure mental health outcomes in different fields, including construction [12,75,80,81]. DASS-21 uses a four-point Likert scale (1—never to 4—almost always) for the measurement. The third section comprised 20 psychosocial hazards based on the findings of Zhang et al. [29], which have been validated in their research. This section used a Likert scale, ranging from 1—very unimportant to 5—very important, to assess the level of the psychosocial hazards amid the most serious perceived period of the nearly three-year pandemic in China.
A mental health academic in construction management and five experienced construction professionals were invited to participate in a pilot study to improve and validate the questionnaire. Based on their feedback, the wording of items in the questionnaire was refined to improve clarity. Through their professional networks and the use of the purposive sampling technique, the researchers invited prospective respondents from construction enterprises in China to complete the survey. Professionals working for design and consulting companies and clients were not included in the survey. In the research process, the online survey link was presented to construction professionals who were asked to share the survey link with their colleagues in their departments to complete the survey. A two-step data screening process was then employed to ensure validity. In the first step, the completeness of each response was evaluated. In the second step, the standard deviation (SD) was calculated. An SD of zero means a respondent picked the same Likert scale point throughout the questionnaire. Such responses were considered invalid and excluded [82]. In total, 551 questionnaires were collected, and 531 samples are valid for analysis. The majority (around 70%) of the respondents were working in the southeast of China. The remaining respondents were working in different areas of China.
The following formula was used to determine the required sample size [83]:
n o = Z 2 p q e 2
In the formula, no refers to the minimum sample size; Z means the Z value, which is related to the chosen confidence interval; p refers to the estimated proportion of population variability; q is 1 − p; and e is the margin of error. To obtain maximum variability, p = 0.5; therefore, the q value is 0.5. Utilizing a 95% confidence level and a margin of error of 5%, the Z value is 1.96. A minimum sample size is 385 respondents according to the calculation results using this formula. The sample size is sufficient for the following analyses, and is larger than existing mental health research in construction (e.g., Sun et al. [19] and Deep et al. [84]). Table 2 presents the profile of the respondents.

3.2. Data Analysis

To determine the internal consistency and instrument reliability, the Cronbach’s alpha coefficients were assessed. Following this, mean scores were calculated in the form of descriptive analysis to determine the importance of the pandemic-induced psychosocial hazards. Then, the levels of depression, anxiety, and stress among these professionals were evaluated, with the prevalence rates of severe to extremely severe mental health levels being analyzed. Subsequently, Spearman’s correlation analysis was used to explore the association between the psychosocial hazards they experienced during the pandemic and their mental health outcomes. As the data were not normally distributed, and the research was intended to develop a theory and was explorative in nature [85,86], partial least-squares structural equation modeling (PLS-SEM) analysis was adopted to explore the association between the psychosocial hazards and the mental health outcomes of construction professionals amid the pandemic. Composite reliability (CR) and average variance extracted (AVE) were employed to evaluate the reliability and internal consistency of the constructs, while heterotrait–monotrait (HTMT) parameters were utilized to ascertain the discriminant validity of the conceptual measurement model [85,87]. The research flow chat is shown in Figure 2.

4. Results

4.1. Reliability and Validity

Statistical Package for Social Science (SPSS) 20.0 and Smart PLS 3.0 were used in the data analysis. The analysis results for Cronbach’s alpha, CR, and AVE are shown in Table 3.
Table 3 demonstrates that the coefficients of Cronbach’s alpha for the pandemic-induced psychosocial hazards and mental health outcomes are beyond the 0.7 threshold, which means that the questionnaire is reliable [88]. Furthermore, all CRs for the measurements of psychosocial hazards and mental health outcomes are above the threshold of 0.7, and all AVEs exceed 0.5, indicating the constructs are reliable and internally consistent for PLS-SEM analysis [85,87,89]. In addition, as indicated in Table 4, all HTMT parameters were less than 1.0, confirming the discriminant validity of the conceptual model [90,91].

4.2. Pandemic-Induced Psychosocial Hazards

The results for the mean values and ranking of psychosocial hazards dimensions and factors are presented in Table 5. This analysis corresponds to the first research objective.
The results indicate that at the dimension level, work–family-related factors were leading pandemic-induced psychosocial hazards, followed by environmental factors and personal factors. In addition, the average values for the three dimensions were all above 3.1, indicating that all three dimensions can be considered moderately important. At the factor level, worrying about relatives’ being infected with COVID-19(WF2), decreased income due to the pandemic (WF5), and social distancing measures due to the pandemic (EF4) were the three most critical psychosocial hazards. Due to strict government measures and the spread of information (and misinformation) by various media, pandemic panic spread globally. They intensified the fear of relatives and family members being infected by COVID-19 [92]. The financial impact of COVID-19, as represented by reduced income due to restrictions and the inability to operate construction projects normally, is similar to an investigation in India by Noopur [51], who found that fear of losing a job, insecurity regarding the organization’s economic survival, and workload and salary were the most significant psychological hazards during the pandemic. Likewise, in their study in the US, Kim et al. [48] revealed that low income was a leading psychological hazard during the pandemic. In addition, except for improper strategies used to cope with the pandemic (PF3), the respondents agreed that all the remaining 19 pandemic-induced psychosocial hazards had certain degrees of impact on their mental health (mean values are over the middle point of three).
The above results indicate that the psychosocial hazards experienced by construction professionals amid the pandemic are different from those during the non-pandemic period, which often emphasize job-related factors as primary hazardous factors. For instance, Chan et al. [75] identified 32 mental health risk factors from 16 journal papers and found that risk factors related to job demand and job control are most prominent for construction workers. Similarly, Tijani et al. [27] obtained 49 psychosocial hazardous factors from 38 journal papers, and found that the most frequently notified mental health stressors are project overload, home–work conflict, poor working environment, project role ambiguity, and poor workgroup relationships. Fordjour et al. [93] also revealed that the potential causes of poor psychological health conditions among construction employees in the Ghanaian construction industry are demanding tasks, poor working relationships, role demands, harsh work conditions, insufficient autonomy, and insufficient feedback, as well as unfair rewards and treatment. Understanding these critical psychosocial hazards is important so that specific measures and management approaches can be implemented to address mental health challenges during public health emergency scenarios.

4.3. Mental Health Outcomes

The levels of depression, anxiety, and stress of the respondents are shown in Table 6. This analysis corresponds to the second research objective. The results are very concerning in terms of the prevalence rates and overall scores. Over 15% of the respondents (15.07%) had severe and extremely severe levels of depression, while nearly 20% of the respondents (18.83%) fell within the severe and extremely severe levels of anxiety. Furthermore, over 10% of the respondents (11.11%) had an extremely severe level of stress. These results demonstrate that the majority of the construction professionals suffered from serious mental illnesses during the pandemic, especially anxiety.
The prevalence rates are higher than those reported in other countries. For instance, Adhikari et al. [39] reported that the prevalence values of depression, anxiety, and stress symptoms are 17.1%, 19.2%, and 16.4% among construction workers in Nepal, respectively. In the current study, on the other hand, the levels of depression, anxiety, and stress, from moderate to extremely severe, are 32.96%, 32.58%, and 16.95%, respectively. Therefore, the professionals working in Chinese construction enterprises might have poorer mental health conditions than construction professionals in other countries during the pandemic. China implemented coordinated and swift control measures, which successfully contained the spread of the virus within a relatively short period of time and supported the recovery of the economy [94]. Despite this mammoth effort, the mental health of construction professionals was still not ensured during this period, indicating the need to further understand sociocultural factors that affect it.
The prevalence rates are also significantly higher than those of the general population (16.5%, 28.8%, and 8.1%) [50] and medical care workers (13.6%, 13.9%, and 8.6%) [95] in China. In addition, Du et al. [96] investigated various types of professions during the pandemic in China and found that the prevalence rates of depression, anxiety, and stress are 17.9%, 30.3%, and 13.7%, respectively, lower than the rates found in this study. This finding reflects the condition during the non-pandemic period, where construction professionals’ mental health is worse than that of other professionals.

4.4. Impacts of Pandemic-Induced Psychosocial Hazards on Mental Health Outcomes

Table 7 shows the analysis results of the correlations between the three dimensions of pandemic-induced psychosocial hazards and mental health outcomes measured by depression, anxiety, and stress, which correspond to the third research objective.
The results show that all the variables are significantly correlated at the 0.01 level (p < 0.01). The three dimensions of psychosocial hazards are significantly correlated, and all the coefficient r(531) values are above 0.7, meaning that all the dimensions are interrelated, in which an increase in one dimension increases the levels in the other two. Specifically, environmental factors and work–family-related factors have the strongest relationship. In this case, research has found that the specific environmental conditions caused by the pandemic increased family violence [97] and reduced the mental health of children due to them witnessing the anxiety manifested by their parents [98]. Similarly, the three categories of mental health problems are also interrelated. This highlights that a comprehensive mental health intervention strategy is needed to improve the overall mental health conditions of these construction professionals. More importantly, the three pandemic-induced psychosocial hazardous dimensions have strong correlations with the mental health outcomes of professionals. Among them, personal factors have the strongest relationships with depression, anxiety, and stress. This is in contrast to the non-pandemic scenario, where work-related factors have the strongest association with poor mental health [12]. Therefore, managing and reducing the occurrence of these psychosocial hazards during the pandemic could contribute to improving the mental health of construction professionals.
In order to further ascertain the impacts of pandemic-induced psychosocially hazardous factors on the mental health outcomes of construction professionals, PLS-SEM analysis was implemented, and the results are shown in Figure 3. A bootstrapping technique was used with 500 rounds. The items loaded on their individual constructs ranged from 0.760 to 0.916, which are acceptable, as they are above the benchmarking value of 0.5 [85,99]. Standardized root mean square residual (SRMR) is 0.04, which is less than the threshold of 0.08 [91].
Figure 3 indicates that among the three dimensions, personal factors were the leading psychosocial hazards that significantly influenced the mental health of these professionals during the pandemic. In addition, personal factors have a stronger influence on anxiety (path coefficient = 0.256, p = 0.000) than stress (path coefficient = 0.235, p = 0.000) and depression (path coefficient = 0.211, p = 0.002), while work–family-related factors have more serious impacts on depression (path coefficient = 0.199, p = 0.004) than anxiety (path coefficient = 0.149, p = 0.018) and stress (path coefficient = 0.191, p = 0.004). Environmental factors do not have strong impacts on mental health, as the coefficients are low, ranging from 0.015 to 0.027, and the levels are not significant.
Furthermore, the results also demonstrate that increased severity of chronic illness and relevant problems due to the pandemic (PF5) and personal traits (PF6) are leading personal factors resulting in poor mental health of Chinese construction professionals. In addition, frequently working overtime due to the pandemic (WF3) and decreased income due to the pandemic (WF5) are primary work–family-related factors leading to mental ill health among these construction professionals. First, because of the strict lockdown and social distancing measures during the pandemic in China, construction professionals with chronic illness might have experienced higher levels of anxiety or stress, as they were worried about whether they could receive regular physical examinations required or obtain necessary medicines. Furthermore, most construction professionals are male and the main providers for their families. Therefore, worrying about being incapacitated due to COVID-19 is a major psychosocial hazard for them. Second, existing research has demonstrated that personal characteristics were significant moderating factors affecting mental health during the pandemic. For instance, a study in Spain [100] found that, during the pandemic, female construction workers suffered from a higher level of psychological distress than their male counterparts, even though they had equal preventive measures and training. Borg et al. [101] also reported that construction professionals with a high level of personal resilience are able to keep working effectively during the pandemic despite disruptions. Third, being compelled to adopt the strict control measures imposed by the government during the pandemic, construction professionals’ workloads escalated considerably [14], leading to their frequently working overtime and subsequent mental health problems. Finally, the pandemic significantly affected the construction industry; for instance, many construction projects were interrupted, delayed, or abandoned. In the US, the construction GDP (gross domestic product) in the second quarter of 2020 decreased by 26.5% [102]. In this investigation, several construction professionals also mentioned that, due to the influence of the pandemic, the actual payment for some completed construction projects was around 50–60% of the payment due, causing cascading effects on the cash flow of these projects and reduced income for professionals.
Contrary to the above results, existing research in other regions has shown that work-related factors have the strongest impact on the mental health of construction professionals during the pandemic. As an example, Sun et al. [19] revealed that among construction professionals, insufficient job control, job insecurity, and lack of supervisor support are significant psychosocial hazards that cause severe mental health problems, while improving the physical working environment has the strongest association with better mental health. Palaniappan et al. [81] found that a lack of supportive supervisors or sympathetic teammates can result in poorer mental health among migrant construction workers in Singapore, while peer support reduces the levels of depression, anxiety, and stress.
The results of this study are also different from some investigations in other regions during the non-pandemic period. For instance, Palaniappan et al. [80] found that ethnicity and inadequate awareness of job scope are significant predictors of depression, anxiety, and stress among construction workers from foreign countries working in Singapore, while lack of job clarity is related to higher levels of stress and anxiety. Nwaogu et al. [3] revealed that if the psychosocial factors related to job demand are reduced by 50%, mental health illness prevalence can be effectively mitigated and prevented. These results reveal that job-related hazards are critical factors causing various mental health problems in the construction industry. Despite these differences, existing research, together with this study, highlights that the associations between psychosocial hazards and mental health are influenced by demographic variables, including regions and occupations.

5. Conclusions

Existing research has examined job-related psychosocial hazards and mental health outcomes of construction professionals. However, psychosocial hazards in relation to the pandemic and their influence on the mental health outcomes of professionals working for construction enterprises have not been adequately investigated. This research, therefore, has achieved its three research objectives by identifying the critical pandemic-induced psychosocial hazards experienced by Chinese construction professionals, measuring their mental health levels, and investigating the influence of these psychosocial hazards on their mental health outcomes.
First, the findings indicate that work–family-related factors and environmental factors were the leading psychosocial hazards during the pandemic. Worrying about relatives’ infection, reduced income, and various social distancing measures were the three most critical pandemic-induced psychosocial hazards. Second, these professionals also suffered from elevated levels of depression, anxiety, and stress. The levels were considerably higher than the levels experienced by the general population and individuals working in other industries in China. The levels were also higher when compared with the levels of construction professionals in other countries. Third, this research also found that personal factors produce the most significant influence on the mental health of these professionals, especially on their anxiety level, which is also the most serious mental health problem measured in the study. Work–family-related factors, on the other hand, are more influential on the development of depression among construction professionals.
These findings are critical to expanding our understanding of the mental health outcomes of construction professionals in the midst of public health emergency scenarios, such as the COVID-19 pandemic. The already poor mental health in the construction industry was further worsened by the pandemic. The results challenge the traditional focus on job-related factors as the primary psychosocial hazards in construction and open new avenues for research into personal and family-related hazards that construction professionals face, particularly in the context of a global pandemic. Practically, this research draws attention to the fact that the professionals working for construction enterprises were particularly vulnerable, as the majority of these professionals worked on construction sites, with various restrictions on site activities, further exacerbating their poor mental health outcomes. This calls for a broader need for societal recognition of the mental health challenges faced by frontline construction professionals in the stressful construction industry. In addition, the identified critical psychosocial hazardous factors can help various government departments and construction enterprises develop more effective risk and disaster management strategies to ensure the mental health of construction professionals. For instance, special care should be devoted to construction professionals with chronic illness, as they are more vulnerable to the impact of public health emergencies like the pandemic.

6. Limitations and Future Research Directions

There are two primary limitations in this study that offer potential avenues for future research. First, similar to other mental health research on construction, the data was collected through a self-report questionnaire survey investigation. Alternative data collection approaches (e.g., medical measurement or interviews) can be employed to further complement the quantitative survey results of this research and to obtain deeper insights into the interrelationships among the variables. Second, the demographic traits of the respondents can be used as variables to further explore the differences and similarities of psychosocial hazards they experienced and the mental health levels among construction professionals. Different models can be developed to ascertain the associations between psychosocial hazards induced by the pandemic and mental health outcomes. These results can be valuable to formulate specific mental health interventions and risk and disaster management mitigation strategies for different construction professionals in the case of future public health emergencies.

Author Contributions

Conceptualization, S.Z., H.L., and P.K.; methodology, S.Z., H.L., Z.S., and P.K.; software, S.Z.; validation, H.L. and P.K.; formal analysis, S.Z., R.Y.S., C.K.H.H., H.L., Z.S., and P.K.; investigation, H.L. and P.K.; resources, Z.S.; data curation, H.L. and P.K.; writing—original draft preparation, S.Z.; writing—review and editing, R.Y.S., C.K.H.H., and Z.S.; visualization, H.L. and P.K.; supervision, S.Z.; project administration, S.Z.; funding acquisition, S.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was financially supported by the Major Project of Philosophy and Social Science Research in Colleges and Universities. Project Number: 2023SJZD011.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of School of Civil Engineering at Suzhou University of Science and Technology (protocol code is not available because the board does not provide a specific code or number for an approved investigation; approved on 1 March 2023).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the corresponding author upon request.

Acknowledgments

Thanks are extended to the professionals who participated in this survey. This paper is part of research that investigates the relationships between psychosocial hazards and mental health in the Chinese construction industry during the pandemic. As such, there may be other articles published with different scopes/objectives while sharing a common background and methodology.

Conflicts of Interest

Author Peng Kang was employed by the company Suzhou No. Two Construction Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Mates in Mind. Mental Health in UK Construction: The Statistics. 2023. Available online: https://www.matesinmind.org/news/mental-health-in-uk-construction-the-statistics (accessed on 30 May 2025).
  2. Dong, X.S.; Brooks, R.D.; Brown, S.; Harris, W. Psychological distress and suicidal ideation among male construction workers in the United States. Am. J. Ind. Med. 2022, 65, 396–408. [Google Scholar] [CrossRef] [PubMed]
  3. Nwaogu, J.M.; Chan, A.P.C.; Akinyemi, T.A. Conceptualizing the dynamics of mental health among construction supervisors. Int. J. Constr. Manag. 2023, 23, 2593–2613. [Google Scholar] [CrossRef]
  4. Jia, B.; Xie, M.; Wu, J.; Zhao, J. Underneath social media texts: Sentiment responses to public health emergency during 2022 COVID-19 pandemic in China. Int. J. Disaster Risk Reduct. 2025, 118, 105239. [Google Scholar] [CrossRef]
  5. Oufi, N.; Garza, C.D.; Nascimento, A. Dynamics of COVID-19 crisis management in hospitals and its long-term effects: An analysis using organizational resilience. Appl. Ergon. 2025, 126, 104486. [Google Scholar] [CrossRef]
  6. Sadeh, H.; Mirarchi, C.; Shahbodaghlou, F.; Pavan, A. Predicting the trends and cost impact of COVID-19 OSHA citations on US construction contractors using machine learning and simulation. Eng. Constr. Archit. Manag. 2023, 30, 3461–3479. [Google Scholar] [CrossRef]
  7. OECD (Organisation for Economic Co-operation and Development). Tackling the Mental Health Impact of the COVID-19 Crisis: An Integrated, Whole-of-Society Response; OECD Publishing: Paris, France, 2021.
  8. Pirzadeh, P.; Lingard, H. Working from Home during the COVID-19 Pandemic: Health and well-being of project-based construction workers. J. Constr. Eng. Manag. 2021, 147, 04021048. [Google Scholar] [CrossRef]
  9. Koren, H.; Milaković, M.; Bubaš, M.; Bekavac, P.; Bekavac, B.; Bucić, L.; Čvrljak, J.; Capak, M.; Jeličić, P. Psychosocial risks emerged from COVID-19 pandemic and workers’ mental health. Front. Psychol. 2023, 14, 1148634. [Google Scholar] [CrossRef]
  10. Barros, C.; Baylina, P.; Fernandes, R.; Ramalho, S.; Arezes, P. Healthcare workers’ mental health in pandemic times: The predict role of psychosocial risks. Saf. Health Work 2022, 13, 415–420. [Google Scholar] [CrossRef]
  11. Alboga, O.; Tantekin-Çelik, G.; Un, B.; Aydinli, S.; Erdis, E. The effects of COVID-19 on the construction sector: Before and after. Int. J. Disaster Risk Reduct. 2025, 119, 105278. [Google Scholar] [CrossRef]
  12. Zhang, S.; Sunindijo, R.Y.; Frimpong, S.; Su, Z. Work stressors, coping strategies, and poor mental health in the Chinese construction industry. Saf. Sci. 2023, 159, 106039. [Google Scholar] [CrossRef]
  13. Zhang, S.; Liu, Q.; Yang, F.; Zhang, J.; Fu, Y.; Zhu, Z.; Guo, J.; Li, X.; Yang, L. Associations between COVID-19 infection experiences and mental health problems among Chinese adults: A large cross-section study. J. Affect. Disord. 2023, 340, 719–727. [Google Scholar] [CrossRef] [PubMed]
  14. Li, Z.; Jin, Y.; Li, W.; Meng, Q.; Hu, X. Impacts of COVID-19 on construction project management: A life cycle perspective. Eng. Constr. Archit. Manag. 2023, 30, 3357–3389. [Google Scholar] [CrossRef]
  15. Liang, Q.; Leung, M.Y.; Zhang, S. Examining the critical factors for managing workplace stress in the construction industry: A cross-regional study. J. Manag. Eng. 2021, 37, 04021045. [Google Scholar] [CrossRef]
  16. Olanrewaju, A.; AbdulAziz, A.; Preece, C.N.; Shobowale, K. Evaluation of measures to prevent the spread of COVID-19 on the construction sites. Clean. Eng. Technol. 2021, 5, 100277. [Google Scholar] [CrossRef]
  17. Pikhart, H.; Pikhartova, J. The Relationship Between Psychosocial Risk Factors and Health Outcomes of Chronic Diseases: A Review of the Evidence for Cancer and Cardiovascular Diseases; WHO Regional Office for Europe: Copenhagen, Denmark, 2015.
  18. Sun, C.; Hon, C.K.H.; Way, K.A.; Jimmieson, N.L.; Xia, B. The relationship between psychosocial hazards and mental health in the construction industry: A meta-analysis. Saf. Sci. 2022, 145, 105485. [Google Scholar] [CrossRef]
  19. Sun, C.; Hon, C.K.H.; Way, K.A.; Jimmieson, N.L.; Xia, B.; Wu, P. A bayesian network model for the impacts of psychosocial hazards on the mental health of site-based construction practitioners. J. Constr. Eng. Manag. 2023, 149, 04022184. [Google Scholar] [CrossRef]
  20. Hon, C.K.H.; Sun, C.; Way, K.A.; Jimmieson, N.L.; Xia, B.; Biggs, H.C. Psychosocial hazards affecting mental health in the construction industry: A qualitative study in Australia. Eng. Constr. Archit. Manag. 2023, 31, 3165–3192. [Google Scholar] [CrossRef]
  21. Rebar, A.L.; Taylor, A. Physical activity and mental health; it is more than just a prescription. Ment. Health Phys. Act. 2017, 13, 77–82. [Google Scholar] [CrossRef]
  22. National Bureau of Statistics. National Data in 2023. 2023. Available online: https://data.stats.gov.cn/easyquery.htm?cn=C01 (accessed on 10 June 2025). (In Chinese)
  23. National Bureau of Statistics. National Data in 2024. 2024. Available online: https://data.stats.gov.cn/easyquery.htm?cn=C01 (accessed on 10 June 2025). (In Chinese)
  24. Lazarus, R.S.; Folkman, S. Stress, Appraisal, and Coping; Springer Publishing Company: New York, NY, USA, 1984. [Google Scholar]
  25. Pirzadeh, P.; Lingard, H.; Zhang, R.P. Job quality and construction workers’ mental health. J. Constr. Eng. Manag. 2022, 148, 04022132. [Google Scholar] [CrossRef]
  26. Liu, D.; Kan, Z.; Kwan, M.P.; Cai, J.; Liu, Y. Assessing the impact of socioeconomic and environmental factors on mental health during the COVID-19 pandemic based on GPS-enabled mobile sensing and survey data. Health Place 2025, 92, 103419. [Google Scholar] [CrossRef]
  27. Tijani, B.; Jin, X.; Osei-Kyei, R. A systematic review of mental stressors in the construction industry. Int. J. Build. Pathol. Adapt. 2020, 39, 433–460. [Google Scholar] [CrossRef]
  28. Frimpong, S.; Sunindijo, R.Y.; Wang, C.; and Boadu, E. Domains of psychosocial risk factors affecting young construction workers: A systematic review. Buildings 2022, 12, 335. [Google Scholar] [CrossRef]
  29. Zhang, S.; Duan, J.; Sunindijo, R.Y. Mental health outcomes and psychosocial hazards experienced by construction professionals with different demographic characteristics during the COVID-19 pandemic. Eng. Constr. Archit. Manag. 2025. [Google Scholar] [CrossRef]
  30. Liang, H.; Liu, T.; Yang, W.; Xia, F. Impact of COVID-19 pandemic perception on job stress of construction workers. Int. J. Environ. Res. Public Health 2022, 19, 10169. [Google Scholar] [CrossRef]
  31. Gao, L.; Deng, X.; Yang, W.; Fang, J. COVID-19 related stressors and mental health outcomes of expatriates in international construction. Front. Public Health 2022, 10, 961726. [Google Scholar] [CrossRef]
  32. Joseph, L.M. Impact of COVID-19 on mental health and emotional well-being of older adults. World J. Virol. 2022, 11, 129–136. [Google Scholar] [CrossRef]
  33. Pamidimukkala, A.; Kermanshachi, S. Impact of COVID-19 on field and office workforce in construction industry. Proj. Leadersh. Soc. 2021, 2, 100018. [Google Scholar] [CrossRef]
  34. Lai, J.; Ma, S.; Wang, Y.; Cai, Z.; Hu, J.; Wei, N.; Wu, J.; Du, H.; Chen, T.; Li, R.; et al. Factors associated with mental health outcomes among health care workers exposed to coronavirus disease 2019. JAMA Netw. Open 2020, 3, e203976. [Google Scholar] [CrossRef]
  35. Preti, E.; Di Mattei, V.; Perego, G.; Ferrari, F.; Mazzetti, M.; Taranto, P.; Pierro, R.D.; Madeddu, F.; Calati, R. The psychological impact of epidemic and pandemic outbreaks on healthcare workers: Rapid review of the evidence. Curr. Psychiatry Rep. 2020, 22, 43. [Google Scholar] [CrossRef]
  36. Guo, Q.; Zheng, Y.; Shi, J.; Wang, J.; Li, G.; Li, C.; Fromson, J.A.; Xu, Y.; Liu, X.; Xu, H.; et al. Immediate psychological distress in quarantined patients with COVID-19 and its association with peripheral inflammation: A mixed-method study. Brain Behav. Immun. 2020, 88, 17–27. [Google Scholar] [CrossRef]
  37. Hossain, M.M.; Tasnim, S.; Sultana, A.; Faizah, F.; Mazumder, H.; Zou, L.; McKyer, E.L.J.; Ahmed, H.U.; Ma, P. Epidemiology of mental health problems in COVID-19: A review. F1000Resarch 2020, 9, 636. [Google Scholar] [CrossRef]
  38. Xiong, J.; Lipsitz, O.; Nasri, F.; Lui, L.M.W.; Gill, H.; Phan, L.; Chen-Li, D.; Iacobucci, M.; Ho, R.; Majeed, A.; et al. Impact of COVID-19 pandemic on mental health in the general population: A systematic review. J. Affect. Disord. 2020, 277, 55–64. [Google Scholar] [CrossRef]
  39. Adhikari, B.; Poudel, L.; Bhandari, N.; Adhikari, N.; Shrestha, B.; Poudel, B.; Bishwokarma, A.; Kuikel, B.S.; Timalsena, D.; Paneru, B.; et al. Prevalence and factors associated with depression, anxiety and stress symptoms among construction workers in Nepal. PLoS ONE 2023, 18, e0284696. [Google Scholar] [CrossRef]
  40. Nochaiwong, S.; Ruengorn, C.; Thavorn, K.; Hutton, B.; Awiphan, R.; Phosuya, C.; Ruanta, Y.; Wongpakaran, N.; Wongpakaran, T. Global prevalence of mental health issues among the general population during the coronavirus dis-ease-2019 pandemic: A systematic review and meta-analysis. Sci. Rep. 2021, 11, 10173. [Google Scholar] [CrossRef] [PubMed]
  41. Yang, T.; Qu, X.L.; Wang, H. Mental health of community residents in Wuhan during the epidemic of COVID-19 and the influencing factors. J. Nurs. Sci. 2020, 35, 76–78. (In Chinese) [Google Scholar]
  42. Mo, Y.; Deng, L.; Zhang, L.; Lang, Q.; Liao, C.; Wang, N.; Qin, M.; Huang, H. Work stress among Chinese nurses to support Wuhan in fighting against COVID-19 epidemic. J. Nurs. Manag. 2020, 28, 1002–1009. [Google Scholar] [CrossRef] [PubMed]
  43. Duckworth, J.; Hasan, A.; Kamardeen, I. Mental health challenges of manual and trade workers in the construction industry: A systematic review of causes, effects and interventions. Eng. Constr. Archit. Manag. 2022, 31, 1497–1516. [Google Scholar] [CrossRef]
  44. Sun, M.Q.; Li, S.S.; Yue, H.Y.; Li, X.; Li, W.; Xu, S.F. Analysis on anxiety status of Chinese netizens under the outbreak of the coronavirus disease 2019(COVID-19) and its influencing factors. Mod. Tradit. Chin. Med. Mater. Medica-World Sci. Technol. 2020, 22, 686–691. (In Chinese) [Google Scholar]
  45. Lin, Z.P.; Wang, L.P.; Yu, B.; Li, H.Q.; Zheng, J.S. Adverse emotional response and its influencing factors among frontline health workers during coronavirus disease 2019 epidemic. Chin. J. Public Health 2020, 36, 677–681. (In Chinese) [Google Scholar]
  46. Su, Y.; Zhou, D. The impact of COVID-19 on physical and mental health: A longitudinal study. SSM—Popul. Health 2023, 24, 101538. [Google Scholar] [CrossRef]
  47. Giuntella, O.; Hyde, K.; Saccardo, S.; Sadoff, S. Lifestyle and mental health disruptions during COVID-19. Proc. Natl. Acad. Sci. USA 2021, 118, e2016632118. [Google Scholar] [CrossRef]
  48. Kim, J.; Linos, E.; Rodriguez, C.I.; Chen, M.L.; Dove, M.S.; Keegan, T.H. Prevalence and associations of poor mental health in the third year of COVID-19: U.S. population-based analysis from 2020 to 2022. Psychiatry Res. 2023, 330, 115622. [Google Scholar] [CrossRef]
  49. Boyraz, G.; Legros, D.N.; Tigershtrom, A. COVID-19 and traumatic stress: The role of perceived vulnerability, COVID-19-related worries, and social isolation. J. Anxiety Disord. 2020, 76, 102307. [Google Scholar] [CrossRef]
  50. Wang, Y.; Di, Y.; Ye, J.; Wei, W. Study on the public psychological states and its related factors during the outbreak of coronavirus disease 2019 (COVID-19) in some regions of China. Psychol. Health Med. 2020, 26, 13–22. [Google Scholar] [CrossRef] [PubMed]
  51. Noopur. Identifying the stressors hindering performance in the Indian construction industry: An empirical investigation. Eng. Constr. Archit. Manag. 2025, 32, 1053–1073. [Google Scholar] [CrossRef]
  52. Carvajal-Arango, D.; Vasquez-Hernandez, A.; Botero-Botero, L.F. Assessment of subjective workplace well-being of construction workers: A bottom-up approach. J. Build. Eng. 2021, 36, 102154. [Google Scholar] [CrossRef]
  53. Zhang, W.R.; Wang, K.; Yin, L.; Zhao, W.F.; Xue, Q.; Peng, M.; Min, B.Q.; Tian, Q.; Leng, H.X.; Du, J.L.; et al. Mental health and psychosocial problems of medical health workers during the COVID-19 epidemic in China. Psychother. Psychosom. 2020, 89, 242–250. [Google Scholar] [CrossRef]
  54. Liang, Y.; Yang, H.; Zhao, J.; Guo, H. Analysis of psychological status and influencing factors of nursing staff in public health emergencies. Hehan Med. Res. 2021, 30, 3652–3656. (In Chinese) [Google Scholar]
  55. Tong, R.; Wang, L.; Cao, L.; Zhang, B.; Yang, X. Psychosocial factors for safety performance of construction workers: Taking stock and looking forward. Eng. Constr. Archit. Manag. 2023, 30, 944–962. [Google Scholar] [CrossRef]
  56. Chung, H.; Birkett, H.; Forbes, S.; Seo, H. COVID-19, flexible working, and implications for gender equality in the United Kingdom. Gend. Soc. 2021, 35, 218–232. [Google Scholar] [CrossRef]
  57. Hallin, H. Home-Based Telework During the COVID-19 Pandemic; Mälardarlen University: Västerås, Sweden, 2020. [Google Scholar]
  58. Setyorini, D.; Swarnata, A.; Bella, A.; Melinda, G.; Dartanto, T.; Kusnadi, G. Social isolation, economic downturn, and mental health: An empirical evidence from COVID-19 pandemic in Indonesia. Ment. Health Prev. 2024, 33, 200306. [Google Scholar] [CrossRef]
  59. Ahmed, S.; Haq, I.; Anam, S.M.A. Impacts of COVID-19 on the construction sector in the least developed countries. Int. J. Build. Pathol. Adapt. 2024, 42, 1085–1105. [Google Scholar] [CrossRef]
  60. King, T.L.; Lamontagne, A.D. COVID-19 and suicide risk in the construction sector: Preparing for a perfect storm. Scand. J. Public Health 2021, 49, 774–778. [Google Scholar] [CrossRef] [PubMed]
  61. Al Dhaheri, A.S.; Bataineh, M.F.; Mohamad, M.N.; Ajab, A.; Al Marzouqi, A.; Jarrar, A.H.; Habib-Mourad, C.; Jamous, D.O.A.; Ali, H.I.; Al Sabbah, H.; et al. Impact of COVID-19 on mental health and quality of life: Is there any effect? A crosssectional study of the MENA region. PLoS ONE 2021, 16, e0247067. [Google Scholar] [CrossRef]
  62. Ettman, C.K.; Abdalla, S.M.; Cohen, G.H.; Sampson, L.; Vivier, P.M.; Galea, S. Prevalence of depression symptoms in US adults before and during the COVID-19 pandemic. JAMA Netw. Open 2020, 3, e2019686. [Google Scholar] [CrossRef]
  63. Fancourt, D.; Steptoe, A.; Bu, F. Trajectories of anxiety and depressive symptoms during enforced isolation due to COVID-19 in England: A longitudinal observational study. Lancet Psychiatry 2021, 8, 141–149. [Google Scholar] [CrossRef]
  64. Choi, E.P.H.; Hui, B.P.H.; Wan, E.Y.F. Depression and anxiety in Hong Kong during COVID-19. Int. J. Environ. Res. Public Health 2020, 17, 3740. [Google Scholar] [CrossRef]
  65. Brooks, S.K.; Webster, R.K.; Smith, L.E.; Woodland, L.; Wessely, S.; Greenberg, N.; Rubin, G.J. The psychological impact of quarantine and how to reduce it: Rapid review of the evidence. Lancet 2020, 395, 912–920. [Google Scholar] [CrossRef]
  66. Choi, S.; Kim, Y.J.; Nam, B.H.; Hong, J.Y.; Cummings, C.E. Perceived vulnerability to disease, resilience, and mental health outcome of Korean immigrants amid the COVID-19 pandemic: A machine learning approach. Nat. Hazards Rev. 2023, 24, 04023001. [Google Scholar] [CrossRef]
  67. Wang, Y.; Shi, L.; Que, J.; Lu, Q.; Liu, L.; Lu, Z.; Xu, Y.; Liu, J.; Sun, Y.; Meng, S.; et al. The impact of quarantine on mental health status among general population in China during the COVID-19 pandemic. Mol. Psychiatry 2021, 26, 4813–4822. [Google Scholar] [CrossRef]
  68. Tull, M.T.; Edmonds, K.A.; Scamaldo, K.M.; Richmond, J.R.; Rose, J.P.; Gratz, K.L. Psychological outcomes associated with stay-at-home orders and the perceived impact of COVID-19 on daily life. Psychiatry Res. 2020, 289, 113098. [Google Scholar] [CrossRef] [PubMed]
  69. Zhao, S.Z.; Wong, J.Y.H.; Wu, Y.; Choi, E.P.H.; Wang, M.P.; Lam, T.H. Social distancing compliance under COVID-19 pandemic and mental health impacts: A population-based study. Int. J. Environ. Res. Public Health 2020, 17, 6692. [Google Scholar] [CrossRef] [PubMed]
  70. Li, Z.; Ge, J.; Yang, M.; Feng, J.; Qiao, M.; Jiang, R.; Bi, J.; Zhan, G.; Xu, X.; Wang, L.; et al. Vicarious traumatization in the general public, members, and nonmembers of medical teams aiding in COVID-19 control. Brain Behav. Immun. 2020, 88, 916–919. [Google Scholar] [CrossRef] [PubMed]
  71. Gao, J.; Zheng, P.; Jia, Y.; Chen, H.; Mao, Y.; Chen, S.; Wang, Y.; Fu, H.; Dai, J. Mental health problems and social media exposure during COVID-19 outbreak. PLoS ONE 2020, 15, e0231924. [Google Scholar] [CrossRef]
  72. People. Notice on Further Optimizing the Prevention and Control Measures of COVID-19 Pandemic. 2022. Available online: http://health.people.com.cn/n1/2022/1111/c14739-32564156.html (accessed on 30 May 2025).
  73. People. The COVID-19 Campaign in China Enters a New Stage—A Record of China’s Optimization of Pandemic Prevention and Control Measures in Accordance with Time and Situation. 2023. Available online: http://health.people.com.cn/n1/2023/0108/c14739-32601966.html (accessed on 30 May 2025).
  74. Qian, J.; Zhang, B.T.; Qiu, W.F.; Jiang, Z.L.; Dong, X.Q.; Li, H.Q. Psychological crisis intervention on the psychological status of COVID-19 patients. China J. Health Psychol. 2021, 29, 101–104. (In Chinese) [Google Scholar]
  75. Chan, A.P.C.; Nwaogu, J.M.; Naslund, J.A. Mental Ill-Health Risk Factors in the construction industry: Systematic review. J. Constr. Eng. Manag. 2020, 146, 04020004. [Google Scholar] [CrossRef]
  76. Tijani, B.; Jin, X.; Osei-Kyei, R. Theoretical model for mental health management of project management practitioners in architecture, engineering and construction (AEC) project organizations. Eng. Constr. Archit. Manag. 2021, 30, 914–943. [Google Scholar] [CrossRef]
  77. Newby, J.M.; Moore, K.O.; Tang, S.; Christensen, H.; Faasse, K. Acute mental health responses during the COVID-19 pandemic in Australia. PLoS ONE 2020, 15, e0236562. [Google Scholar] [CrossRef]
  78. Khan, A.N.; Khan, N.A.; Soomro, M.A. The impact of moral leadership on construction employees’ psychological behaviours. IEEE Trans. Eng. Manag. 2020, 69, 2817–2825. [Google Scholar] [CrossRef]
  79. Lovibond, S.H.; Lovibond, P.F. Manual for the Depression Anxiety Stress Scales, 2nd ed.; Psychology Foundation: Sydney, Australia, 1995. [Google Scholar]
  80. Palaniappan, K.; Natarajan, R.; Dasgupta, C. Prevalence and risk factors for depression, anxiety, and stress among foreign construction workers in Singapore—A cross-sectional study. Int. J. Constr. Manag. 2023, 23, 2479–2487. [Google Scholar] [CrossRef]
  81. Palaniappan, K.; Rajaraman, N.; Ghosh, S. Effectiveness of peer support to reduce depression, anxiety and stress among migrant construction workers in Singapore. Eng. Constr. Archit. Manag. 2023, 30, 4867–4880. [Google Scholar] [CrossRef]
  82. Zheng, J.; Feng, C.; Xie, H.; Zhao, X.; Wu, G. Ambidextrous leadership and innovative behaviors in construction projects: Dual-edged sword effects and social information processing perspective. J. Manag. Eng. 2023, 39, 04022070. [Google Scholar] [CrossRef]
  83. Cochran, W.G. Sampling Technique, 2nd ed.; John Wiley and Sons Inc.: New York, NY, USA, 1963. [Google Scholar]
  84. Deep, S.; Sonkar, N.; Yadav, P.; Vishnoi, S.; Bhoola, V.; Kumar, A. Factors influencing the mental health of white-collar construction workers in developing economies: Analytical study during the COVID-19 pandemic in India. J. Constr. Eng. Manag. 2024, 150, 04024058. [Google Scholar] [CrossRef]
  85. Hair, J.F.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 3rd ed.; Sage Publications: Thousand Oaks, CA, USA, 2022. [Google Scholar]
  86. Willaby, H.W.; Costa, D.S.J.; Burns, B.D.; Maccann, C.; Roberts, R.D. Testing complex models with small sample sizes: A historical overview and empirical demonstration of what Partial Least Squares (PLS) can offer differential psychology. Personal. Individ. Differ. 2015, 84, 73–78. [Google Scholar] [CrossRef]
  87. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  88. George, D.; Mallery, P. SPSS for Windows Step by Step: A Simple Guide and Reference. 11.0 Update, 4th ed.; Allyn and Bacon: Boston, MA, USA, 2003. [Google Scholar]
  89. Nunnally, J.C. Psychometric Theory; McGraw-Hill: New York, NY, USA, 1978; Volume 2. [Google Scholar]
  90. Estudillo, B.; Forteza, F.J.; Carretero-Gomez, J.M.; Rejon-Guardia, F. The role of organizational factors in promoting workers’ health in the construction sector: A comprehensive analysis. J. Saf. Res. 2024, 88, 41–55. [Google Scholar] [CrossRef]
  91. Kim, B.; Chung, J. Is safety education in the E-learning environment effective? Factors affecting the learning outcomes of online laboratory safety education. Saf. Sci. 2023, 168, 106306. [Google Scholar] [CrossRef]
  92. Koçak, O.; Koçak, Ö.E.; Younis, M.Z. The Psychological Consequences of COVID-19 Fear and the Moderator Effects of Individuals’ Underlying Illness and Witnessing Infected Friends and Family. Int. J. Environ. Res. Public Health 2021, 18, 1836. [Google Scholar] [CrossRef]
  93. Fordjour, G.A.; Chan, A.P.C.; Tuffour-Kwarteng, L. Exploring construction employees’ perspectives on the potential causes of psychological health conditions in the construction industry: A study in Ghana. Int. J. Constr. Educ. Res. 2020, 17, 373–393. [Google Scholar] [CrossRef]
  94. Cheng, Z.J.; Zhang, Z.; Xue, M.; Zheng, P.; Lyu, J.; Ma, J.; Zhang, X.D.; Luo, W.; Huang, H.; Zhang, Y.; et al. Public Health Measures and the Control of COVID-19 in China. Clin. Rev. Allergy Immunol. 2023, 64, 1–16. [Google Scholar] [CrossRef]
  95. Si, M.Y.; Su, X.Y.; Jiang, Y.; Wang, W.J.; Gu, X.F.; Ma, L.; Li, J.; Zhang, S.K.; Ren, Z.F.; Ren, R.; et al. Psychological impact of COVID-19 on medical care workers in China. Infect. Dis. Poverty 2020, 9, 113. [Google Scholar] [CrossRef]
  96. Du, J.; Mayer, G.; Hummel, S.; Oetjen, N.; Gronewold, N.; Zafar, A.; Schulttz, J.H. Mental health burden in different professions during the final stage of the COVID-19 lockdown in China: Cross-sectional survey study. J. Med. Internet Res. 2020, 22, e24240. [Google Scholar] [CrossRef]
  97. Zhang, H. The influence of the ongoing COVID-19 pandemic on family violence in China. J. Fam. Violence 2022, 37, 733–743. [Google Scholar] [CrossRef]
  98. Ma, J.; Ding, J.; Hu, J.; Wang, K.; Xiao, S.; Luo, T.; Yu, S.; Liu, C.; Xu, Y.; Liu, Y.; et al. Children and Adolescents’ Psychological Well-Being Became Worse in Heavily Hit Chinese Provinces during the COVID-19 Epidemic. J. Psychiatry Brain Sci. 2021, 6, e210020. [Google Scholar] [CrossRef]
  99. Chin, W.W. The partial least squares approach for structural equation modelling. In Modern Methods for Business Research; Macrolides, G.A., Ed.; Lawrence Erlbaum Associates: Mahwah, NJ, USA, 1998. [Google Scholar]
  100. Gómez-Salgado, C.; Camacho-Vega, J.C.; Allande-Cussó, R.; Ruiz-Frutos, C.; Ortega-Moreno, M.; Linares-Manrique, M.; García-Iglesias, J.J.; Fagundo-Rivera, J.; Rodríguez-Díaz, L.; Vázquez-Lara, J.M.; et al. Psychological distress and work engagement of construction workers during the COVID-19 pandemic: A differential study by sex. Buildings 2024, 14, 773. [Google Scholar] [CrossRef]
  101. Borg, N.; Scott-Young, C.M.; Naderpajouh, N.; Borg, J. Surviving adversity: Personal and career resilience in the AEC industry during the COVID-19 pandemic. Constr. Manag. Econ. 2023, 41, 361–378. [Google Scholar] [CrossRef]
  102. Bureau of Economic Analysis. Real Gross Domestic Product by Industry Group. 2020. Available online: https://www.bea.gov/sites/default/files/2020-12/gdp3q20_3rd.pdf (accessed on 30 May 2025).
Buildings 15 04339 g001
Figure 1. Conceptual model.
Figure 1. Conceptual model.
Buildings 15 04339 g001
Buildings 15 04339 g002
Figure 2. Research flow chat.
Figure 2. Research flow chat.
Buildings 15 04339 g002
Buildings 15 04339 g003
Figure 3. PLS-SEM analysis results. (Note: PF = personal factor, WF = work–family factor, EF = environmental factor.).
Figure 3. PLS-SEM analysis results. (Note: PF = personal factor, WF = work–family factor, EF = environmental factor.).
Buildings 15 04339 g003
Table 1. Pandemic-induced psychosocial hazards from the literature.
Table 1. Pandemic-induced psychosocial hazards from the literature.
HazardLiterature Source
The first dimension: Personal factors (PFs)Worrying about infection with COVID-19 disease (PF1)[30,31,32,33,34,35,36,37,38]
Less sleep due to the pandemic (e.g., having a problem falling asleep or reduced sleep time) (PF2)[8,38,39,40,41,42]
Improper strategies used to cope with the pandemic (e.g., denial, avoidance, smoking, or alcohol consumption) (PF3)[30,35,38,43,44,45]
Reduced exercise due to the pandemic (PF4)[8,38,41,46,47]
Increased severity of chronic illness and corresponding problems due to the pandemic (e.g., enhanced level of possibility of infection, or poorer situation of previous illness) (PF5)[11,31,38,44,48]
Personal traits (e.g., age, gender, and vulnerability) (PF6)[38,48,49,50]
The second dimension: Work–family-related factors (WFs)Significantly increased workload due to the pandemic (WF1)[8,42,51,52,53]
Worrying about relatives’ infection with COVID-19 disease (WF2)[8,31,34,35,38,42,54]
Frequently working overtime due to the pandemic (WF3)[8,34,51,52]
Work–life conflict due to the pandemic (WF4)[8,42,52,55,56,57]
Decreased income due to the pandemic (WF5)[26,30,38,51,58,59,60,61,62]
Inadequate career development opportunities due to the pandemic (WF6)[30,51,58,60]
The third dimension: Environmental factors (EFs)Inadequate care from government departments or employers for minimizing the influence of the pandemic (EF1)[35,42,53,63]
Inadequate epidemic prevention supplies (e.g., medicine or masks) (EF2)[31,53,58,59,64,65]
Discrimination due to infection or possible infection with COVID-19 disease (EF3)[31,58,60,66]
A variety of social distancing measures due to the pandemic (e.g., quarantine or restricted travel) (EF4)[30,38,58,67,68,69]
Too much negative information associated with the pandemic (EF5)[8,30,38,70,71]
Too much false information associated with the pandemic (EF6)[30,38]
More personal information reported to government due to the pandemic (e.g., frequently reporting personal health conditions to authorities) (EF7)[72,73]
More nucleic acid tests due to the pandemic (EF8)[72,73,74]
Table 2. Profile of the respondents.
Table 2. Profile of the respondents.
CategoryProfilePercentage
GenderMale80.23%
Female19.77%
AgeAged less than 20 3.58%
Aged between 21 and 30 40.11%
Aged between 31 and 40 39.36%
Aged between 41 and 50 10.92%
Aged over 50 6.02%
Type of companyState-owned company56.69%
Private company42.56%
Other types of company0.75%
Years of working experienceLess than 1 year 10.17%
Between 1 and 10 years 54.43%
Between 11 and 20 years 26.74%
More than 20 years 8.66%
PositionEmployee without management title 62.90%
Head of department/unit33.15%
Senior manager 3.77%
Other positions0.19%
Main working locationOffice on construction site or construction site68.93%
Office at headquarters or branch company 30.70%
Other working locations0.38%
Table 3. Analysis results for Cronbach’s Alpha, CR, and AVE.
Table 3. Analysis results for Cronbach’s Alpha, CR, and AVE.
ConstructsCronbach’s AlphaCR (rho_a)CR (rho_c)AVE
Personal factors0.9160.9430.9350.705
Work–family factors0.9280.9500.9430.735
Environmental factors0.9550.9580.9620.759
Depression0.9460.9220.9560.758
Anxiety0.9390.9460.9510.735
Stress0.9440.9410.9540.750
Table 4. Analysis results for HTMT parameters.
Table 4. Analysis results for HTMT parameters.
ConstructsPFWFEFDepressionAnxietyStress
Personal factors (PFs)
Work–family factors (WFs)0.845
Environmental factors (EFs)0.8010.853
Depression0.4100.3990.356
Anxiety0.4090.3730.3380.963
Stress0.4300.4120.3680.9700.985
Table 5. Mean values and ranking of pandemic-induced psychosocial hazards.
Table 5. Mean values and ranking of pandemic-induced psychosocial hazards.
HazardsMean ValueRank
The first dimension: Personal factors (PFs)3.121-
Worrying about being infected with COVID-19 (PF1)3.27911
Less sleep due to the pandemic (PF2)3.10719
Improper strategies used to cope with the pandemic (PF3)2.83620
Reduced exercise due to the pandemic (PF4)3.17117
Increased severity of chronic illness and corresponding problems due to the pandemic (PF5)3.17316
Personal traits (PF6)3.16018
The second dimension: Work–family-related factors (WFs)3.371-
Significantly increased workload due to the pandemic (WF1)3.25814
Worrying about relatives being infected with COVID-19 (WF2)3.6211
Frequently working overtime due to the pandemic (WF3)3.27512
Work–life conflict due to the pandemic (WF4)3.18115
Decreased income due to the pandemic (WF5)3.4802
Inadequate career development opportunities due to the pandemic (WF6)3.4095
The third dimension: Environmental factors (EFs)3.351-
Inadequate care from government departments or employers for minimizing the influence of the pandemic (EF1)3.29410
Inadequate epidemic prevention supplies (EF2)3.3587
Discrimination due to infection or possible infection with COVID-19 disease (EF3)3.26913
A variety of social distancing measures due to the pandemic (EF4)3.4223
Too much negative information associated with the pandemic (EF5)3.3418
Too much false information associated with the pandemic (EF6)3.3189
More personal information reported to government due to the pandemic (EF7)3.4164
More nucleic acid tests due to the pandemic (EF8)3.3866
Table 6. Levels of depression, anxiety, and stress.
Table 6. Levels of depression, anxiety, and stress.
Mental Health LevelDepressionAnxietyStress
Percentage of RespondentsRange of ScalePercentage of RespondentsRange of ScalePercentage of RespondentsRange of Scale
Normal56.69%0–454.43%0–376.46%0–7
Mild10.36%5–612.99%4–56.59%8–9
Moderate17.89%7–1013.75%6–75.84%10–12
Severe4.52%11–134.52%8–96.21%13–16
Extremely severe10.55%14+14.31%10+4.90%17+
Overall score5.023 4.612 5.365
Note: The Likert scale was converted to 0–3 in the DASS-21 standard survey instrument to measure the prevalence levels.
Table 7. Spearman’s correlation analysis results.
Table 7. Spearman’s correlation analysis results.
VariablesPFWFEFStressAnxietyDepression
Personal factors (PFs)1
Work–family factors (WFs)0.754 (**)1
Environmental factors (EFs)0.722 (**)0.784 (**)1
Stress0.369 (**)0.368 (**)0.309 (**)1
Anxiety0.328 (**)0.319 (**)0.267 (**)0.874 (**)1
Depression0.344 (**)0.343 (**)0.277 (**)0.874 (**)0.827 (**)1
** Correlation is significant at the 0.01 level (2-tailed).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zhang, S.; Sunindijo, R.Y.; Hon, C.K.H.; Li, H.; Su, Z.; Kang, P. Impact of Pandemic-Induced Psychosocial Hazards on the Mental Health Outcomes of Construction Professionals. Buildings 2025, 15, 4339. https://doi.org/10.3390/buildings15234339

AMA Style

Zhang S, Sunindijo RY, Hon CKH, Li H, Su Z, Kang P. Impact of Pandemic-Induced Psychosocial Hazards on the Mental Health Outcomes of Construction Professionals. Buildings. 2025; 15(23):4339. https://doi.org/10.3390/buildings15234339

Chicago/Turabian Style

Zhang, Shang, Riza Yosia Sunindijo, Carol K. H. Hon, Haoxiang Li, Zhenwen Su, and Peng Kang. 2025. "Impact of Pandemic-Induced Psychosocial Hazards on the Mental Health Outcomes of Construction Professionals" Buildings 15, no. 23: 4339. https://doi.org/10.3390/buildings15234339

APA Style

Zhang, S., Sunindijo, R. Y., Hon, C. K. H., Li, H., Su, Z., & Kang, P. (2025). Impact of Pandemic-Induced Psychosocial Hazards on the Mental Health Outcomes of Construction Professionals. Buildings, 15(23), 4339. https://doi.org/10.3390/buildings15234339

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop