Psychometric Validation of the CD-RISC-10 Among Chinese Construction Project High-Place Workers

Abstract

Individuals with high psychological resilience cope with stress more effectively. It is crucial to select a suitable psychological resilience tool for workers in high-risk industries to identify and help those with lower resilience early on, protecting their health and reducing accidents. The CD-RISC-10 is widely used, and this study assessed its validity and reliability among Chinese construction workers, focusing on workers on elevated platforms. A total of 325 valid CD-RISC-10 scales were collected and analyzed using statistical methods, such as exploratory factor analysis, confirmatory factor analysis, and K-means cluster analysis. The results show that the CD-RISC-10 can effectively measure psychological resilience with a high scale reliability of 0.857, and it had an acceptable model fit (CFI = 0.947) and good item discrimination. About 17.23% of the measured sample of Chinese workers working at height were identified as having resilience impairments, and demographic variables such as age, length of service, educational level, and accident experience had a significant impact on the level of resilience, revealing the heterogeneity of the workers. This study validated the measurement validity of the CD-RISC-10 scale among Chinese high-place workers, and the analysis results were conducive to conducting psychological resilience assessments, improving workers’ occupational health, and promoting the sustainable development of construction enterprises.

1. Introduction

As an important psychological resource for individuals to cope with stress and challenges, resilience has been shown to promote mental health and help individuals maintain adaptability in the face of risk [1,2]. The level of psychological resilience can to some extent determine whether an individual has good coping skills and adaptability when faced with a crisis [3]. Definitions of psychological resilience are usually divided into three categories. Masten [4] defined psychological resilience as a person’s capacity to respond effectively to significant stress or grave risks in order to achieve a better outcome. Garmezy et al. [5] argued that psychological resilience is the ability to remain in a state of normality or to quickly return to the level experienced before experiencing distress. Kalisch et al. [6] argued that psychological resilience is not a stable individual trait, nor a specific genotype or brain structure, but a dynamic process of adapting to a specific stressful environment, depending on complex factors such as individual traits and the nature of the stress. The research in this paper is based on the process definition. In recent years, researchers developed various measurement tools to assess individual psychological resilience. Among them, the Connor–Davidson Resilience Scale (CD-RISC) is one of the most commonly used tools, which includes 25 items and has been simplified into 10-item and 2-item versions. The 10-item Connor–Davidson Resilience Scale (CD-RISC-10) has been widely used due to its high practicality and reliability [7,8,9,10]. It has shown good psychometric properties in Chinese students and depressive patients [11], young Portuguese-speaking college students [12], and individuals with spinal cord injury [13].

High-place work, which is performed at a height of 2 m or more from the reference plane of the fall height, is a common form of work in construction enterprises and other high-risk industries. High-place work not only involves a wide range of industries but has also become an important research topic in the field of industrial safety due to its high-risk nature, high accident mortality rate, and susceptibility to environmental factors [14]. When workers work at height, due to the complexity and variability of their operating environment (e.g., weather, pole surface, etc.), it is easy for fluctuations in the physiological and psychological state of the operators to occur, which makes work-at-height accidents occur frequently. Niu et al. [15] used the risk coupling analysis model to analyze various factors that cause falling accidents; Sajja et al. [16] combined the decision-making trial and evaluation laboratory (DEMATEL) technique integrated with the analytic network process (ANP) for determining causal relationships and prioritization among factors affecting falls in building construction; Abderrahi et al. [17] used machine learning to create a prediction model that detects the probable factors impacting fatal falls from height accidents in the Malaysian construction industry. Goh et al. [18] investigated the considerable influence of cognitive elements on the risky behavior of high-place employees using methods like multiple linear regression, decision trees, and artificial neural networks. Research has shown that the causes of high-place falling accidents are mostly related to extreme psychological stress, which in turn leads to individuals’ behavioral errors [19].

Studies have used the CD-RISC-10 to measure resilience in healthcare workers [20,21,22] due to the influence of COVID-19. Eleonora et al. administered the CD-RISC-10 mental toughness measure to a sample of workers comprising employees, freelancers, entrepreneurs, traders, and managers [23]. However, there is a relative lack of research on the psychological resilience among workers in the construction industry, especially those who are under high pressure in high-place operations for a long time whilst working at a construction site. In addition, the factors affecting psychological resilience also include general demographic factors. Cynthia et al. [24] detected that female nurses had higher levels of psychological resilience than male nurses. Chen et al. [25] found that the older the age, the higher the level of psychological resilience. Yang et al. [18] conducted a cross-sectional survey, and the findings indicated that the impact of literacy on psychological resilience varied significantly, with higher levels of education correlated with higher levels of psychological resilience. Thus, it is necessary to measure the psychological resilience of Chinese high-place workers in construction industry, as well as their grading statistics.

Therefore, this study used the 10-item Connor–Davidson Resilience Scale (CD-RISC-10) as a measurement tool, verified the reliability and validity of the CD-RISC-10 among Chinese high-place workers, and further identified different typical categories of psychological resilience through K-means cluster analysis and analyzed their relationship with high-place operation safety. The results not only provide a new perspective for understanding the psychological safety status of high-altitude workers but also provide a scientific basis for subsequent improvements in the psychological resilience and poor psychological state of workers, which is of great practical significance for reducing the accident rate of high-place operations and ensuring workers’ safety and occupational health.

2. Materials and Methods

2.1. Research Design

Quantitative research methods were used in this study. The Chinese construction project high-place workers were used as the study population, and the CD-RISC-10 was used as the research tool. The CD-RISC-10 was developed by scholars Campbell-Sills et al. [7] and has been validated by examples. This study translated it to the Chinese CD-RISC-10 scale. In the translation process, we strictly followed the standard process of scale translation. Five experts with extensive experience in the fields of psychology, cross-cultural research, and scale assessment were invited to conduct an in-depth discussion and culturally competent assessment of the original English version of the CD-RISC-10 scale. This step was designed to identify possible cultural differences and sensitivity issues. While ensuring the accuracy of the translation, some of the expressions in the scale were appropriately revised according to the Chinese cultural background and linguistic conventions. The modified Chinese scale was then translated into English to check for expression errors. The scoring method adopts the Likert 5-point scale, with “1 point” indicating complete disagreement and “5 points” indicating complete agreement. Additionally, the total score of the scale ranges from 10 to 50 points, with higher scores indicating better psychological resilience.

The initially modified CD-RISC-10 scale was then distributed to and collected from subjects via an online platform. This study first analyzed the demographic characteristics of the sample and conducted a series of statistical analyses on the scale to assess its validity and reliability among Chinese construction project high-place workers. Statistical analysis included item discrimination, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), reliability analysis, K-means clustering analysis, ANOVA, independent samples t-test, and multiple linear regression.

2.2. Participants and Data Collection

The subjects chosen for this study were voluntary, and the questionnaire was distributed only after securing the informed consent of the participants. The eligibility criteria for participant inclusion were as follows: (1) engagement in work at a falling height of 2 m or greater, in accordance with the standards set by GB/T 3608-2008 [26]; (2) age between 18 and 55 years; and (3) a minimum of 1 year of experience working at height.

The sum of 348 questionnaires were recycled online, and after removing invalid questionnaires that were incompletely filled out or had a very short response time (less than 40 s), a total of 325 valid questionnaires were ultimately recovered, with a valid recovery rate of 93.3%.

To investigate the demographic information, the main information collected included gender, age, length of service, educational level, and accident experience. The results of the descriptive statistical research on demographic data are shown in

Table 1. Summary of sociodemographic factors. (n = 325).

Statistical Content	Sample Classification	Number of Samples	Percentage (%)
Gender	Male	325	100
Age	Under 30 years old	54	16.6
	31~40 years old	137	42.2
	41~50 years old	78	24.0
	Over 51 years old	56	17.2
Length of service	Less than 10 years	68	20.9
	11~20 years	132	40.6
	21~30 years	80	24.6
	Over 31 years	45	13.8
Educational level	Junior high school and below	6	1.8
	High school, vocational high school, technical secondary school	45	13.8
	Junior college	60	18.5
	Undergraduate	206	63.4
	Graduate and above	8	2.5
Accident experience	Yes	56	17.2
	No	269	82.8

.

2.3. Data Analysis

This study follows the following four steps to analyze the data.

Step 1: item discrimination and EFA were conducted by SPSS 24.0.

The item discrimination is an important part of the preparation and evaluation of the scale. The main purpose is to explore whether the questions can effectively distinguish between high and low groups, and to test the homogeneity between different questions, so as to judge the accuracy and reliability of the specific questions of the scale. The items were analyzed and screened mainly through the critical ratio method and internal consistency reliability. Firstly, the samples were sorted based on the total score, with scores of 27% and 73% as the boundary, and the survey subjects were divided into high and low subgroups. After that, the two sets of data were subjected to an independent samples t-test. EFA was then conducted to initially validate the structural validity of the scale and determine the number of dimensions among Chinese high-rise workers. The Kaiser–Meyer–Olkin (KMO) test and Bartlett’s test of sphericity were first required determine whether factor analysis could be performed. The common components of the sample were extracted using principal component analysis (PCA), and subsequently the original and rotated factor loading matrices were obtained using the varimax rotation method. The criteria for determining the structure and number of the psychological resilience scale factors were as follows: (1) The factor initial eigenvalue is greater than or equal to 1; (2) The project factor load is greater than or equal to 0.5; (3) At least 2 items are included in each factor; (4) There is no double load on both factors for the same project.

Step 2: CFA was conducted by using AMOS 24.0. The degree of fitness of the actual data from the scale and the theoretical model was verified using the validity. CFA was performed to confirm the scale’s structural model. When using the structural equation to analyze data, comparative fit index (CFI), normative fit index (NFI) and other fitting indexes are commonly used as the evaluation indicators of the fitting degree of measurement. Research has shown that the acceptable range of CFA indices is χ² to df ≤ 3, RMSEA < 0.6 to 0.8, NFI ≥ 0.95, CFI ≥ 0.95, IFI ≥ 0.95, TLI ≥ 0.95, AGFI < 0.95 [27].

Step 3: reliability analysis was then conducted by using SPSS 24.0. The concept of reliability was used to evaluate the consistency and dependability of the survey data’s findings. Internal consistency reliability is generally used as a test to indicate the level of reliability of the test [28]. The internal consistency of the scale was examined in this article using Cronbach’s α, while the split-half reliability coefficient was a supplement to Cronbach’ s α coefficient. When the Cronbach’s α coefficient value is more than 0.7, it often means that the scale’s data outputs have strong internal consistency and validity. Based on the CFA, reliability analysis was performed using SPSS 24.0 and the final scale was obtained.

Step 4: K-means clustering analysis, ANOVA, independent sample t-test, and multiple linear regression were finally performed using SPSS 24.0 to identify different typical categories of psychological resilience and to analyze their relationship with work-at-height safety.

3. Results

3.1. Item Discrimination

The statistical results show that the high group refers to samples with scores above 42 points (42.0280 ± 2.97), while the low group refers to samples with scores below 33 points (32.1143 ± 2.99). Item discrimination is presented in

Table 2. Item discrimination test.

Item	t	df	Sig.	r	SMC	Coefficient After Deleting Item
A1	9.588	174	<0.001	0.422	0.179	0.836
	9.588	144.674	<0.001
A2	11.791	174	<0.001	0.509	0.260	0.828
	11.791	165.731	<0.001
A3	9.038	174	<0.001	0.466	0.218	0.831
	9.038	162.461	<0.001
A4	10.928	174	<0.001	0.523	0.273	0.826
	10.928	161.869	<0.001
A5	11.242	174	<0.001	0.545	0.298	0.824
	11.242	161.723	<0.001
A6	13.219	174	<0.001	0.596	0.355	0.819
	13.219	161.649	<0.001
A7	10.611	174	<0.001	0.511	0.261	0.828
	10.611	168.199	<0.001
A8	12.698	174	<0.001	0.590	0.348	0.820
	12.698	158.942	<0.001
A9	11.479	174	<0.001	0.601	0.361	0.819
	11.479	155.485	<0.001
A10	13.152	174	<0.001	0.588	0.346	0.821
	13.152	161.754	<0.001

. It indicates that the t of all entries was greater than the minimum standard value of 3, and all entries were significant (p < 0.001), which means the items have a high degree of differentiation and good discriminatory ability. The Cronbach’s coefficient of the total scale is 0.857. When an item is deleted, the Cronbach’s α coefficient decreases, indicating that each item contributes to the overall reliability of the scale. Additionally, although the SMC value of item A1 is less than 0.2, the corrected item-total correlation for all items is greater than 0.4, further supporting the retention of all items. Therefore, all items could be retained.

3.2. Exploratory Factor Analysis

A total of 325 valid questionnaires were collected in this study, which met the sample size requirement for factor analysis [27]. The results of KMO and Bartlett’s test of sphericity are shown in

Table 3. KMO and Bartlett’s test of sphericity.

KMO Measure of Sampling Adequacy		0.901
Bartlett’s Test of sphericity	Approx. chi-square	1000.329
	Degrees of freedom (df)	45
	Significance (Sig.)	<0.001

.

The KMO coefficient is 0.901 according to the metrics of KMO and Bartlett’s test of sphericity, which shows a significant correlation between the independent and dependent variables. The significance level of Bartlett’s test of sphericity is <0.05, which qualified for further factor analysis.

The analysis of the Scree test showed that the plot line started to level off after the second factor; therefore, keeping one to two components was best. When assuming a dual factor structure, only one results with an initial eigenvalue of the factor greater than 1. Items A1 and A2 exhibited dual loads, which did not meet the criteria for determining the factor structure and number. This indicated that the two-factor structure was unstable; therefore, the factor structure of CD-RISC-10 was unidimensional. The loadings of the items were in accordance with the requirements, and the initial eigenvalue was 4.401. The cumulative percentage of variance was 88.109%, and the single-factor structure has higher stability than the two-factor structure. All items had factor loadings that were higher than 0.5, and they varied from 0.555 to 0.747. The specific results are shown in

Table 4. Factor load matrix (n = 325).

Item	Factor Loading	Communality Values
A1	0.555	0.764
A2	0.586	0.871
A3	0.678	0.728
A4	0.685	0.798
A5	0.687	0.922
A6	0.712	0.890
A7	0.582	0.790
A8	0.681	0.891
A9	0.694	0.904
A10	0.747	0.890
Eigenvalue	4.401
Cumulative of Variance (%)	88.109

.

Through exploratory factor analysis, the dimension division of the scale was obtained when applied to construction workers. The scale has a stable single-dimensional structure, identical to that of the scale when applied to other groups [9].

3.3. Confirmatory Factor Analysis and Reliability Analysis

Table 5. Overall fitting coefficient results.

Structural Model	χ2/df	RMSEA	NFI	CFI	IFI	TLI	AGFI
CD-RISC-10	2.458	0.067	0.915	0.947	0.948	0.932	0.922

and

Table 6. Factor load and composite reliability.

Route			Estimate	AVE	CR (Combination Reliability)
A1	←	A	0.485	0.612	0.8579
A2	←	A	0.520
A3	←	A	0.631
A4	←	A	0.637
A5	←	A	0.634
A6	←	A	0.672
A7	←	A	0.525
A8	←	A	0.641
A9	←	A	0.654
A10	←	A	0.720

present the results of the fit of the structural model to the data, factor loading, and composite reliability.

As can be seen from Table 5 and Table 6, the main fitting indicators met the requirements of the scale structure model fitting. All factor loading coefficients were greater than 0.4, and the AVE value was 0.612. The scale’s convergent validity and combination reliability (CR), which were more than 0.7 and equal to 0.8579, showed that the model and data of a single dimension of the CD-RISC-10 fit well.

The reliability analysis results showed that the split-half reliability coefficient was 0.817, and the final overall reliability of the scale was determined to be 0.857, which means that the CD-RISC-10 has good reliability among Chinese construction project high-place workers.

3.4. Psychological Characteristics of High-Place Workers

3.4.1. Clustering Results for Psychological Resilience

Descriptive statistics showed that the psychological resilience score of workers was (35.07 ± 4.247), slightly higher than the psychological resilience level of ordinary adults (32.7 ± 6.95) but significantly lower than the psychological resilience level of the general population in the United States (40.2 ± 6.74) [29].

The K-means clustering algorithm has the advantages of simplicity, efficiency, ease of implementation, and significant clustering effects on big data, making it a commonly used clustering analysis method. When analyzing the grading of workers’ mental toughness levels, the use of the K-means clustering algorithm can effectively divide the data points into multiple clusters, so that the data points (i.e., the mental toughness characteristics of workers) within the same cluster have a high degree of similarity, while the data points between different clusters differ greatly. Based on the iteration results, three clusters were ultimately determined, as shown in

Table 7. Cluster centers for K-means clustering analysis of psychological resilience.

Research Variables	Cluster 1 (N = 56)	Cluster 2 (N = 41)	Cluster 3 (N = 228)
Total score of psychological resilience	30	45	38

.

Cluster 1 has 56 (17.23%) research subjects, showing difficulty handling negative events in their work. Cluster 2 has 41 members (12.62%), showing strong adaptability to unexpected situations, active ability to handle pressure, and optimistic handling of situations. Cluster 3 has 228 members (70.15%), indicating that they tend to handle transactions with a positive attitude in most cases. The CD-RISC-25 divides the level of psychological resilience into four levels: poor, average, good, and excellent. Based on this, it is determined that Cluster 2 and Cluster 3 have normal levels of psychological resilience, accounting for 82.77% of the total survey subjects. Cluster 1 is a collection of individuals with poor psychological resilience, mainly including “difficulties in recovering from setbacks or difficulties”, “inability to concentrate and think when facing pressure”, “susceptibility to being knocked down by failure”, and “significant emotional fluctuations”, which is classified as a psychological resilience disorder.

3.4.2. Analysis of Influencing Factors

The effects of age, length of service, educational level, and whether there has been any accident experience on psychological resilience were examined based on demographic factors. Accident experience was analyzed by t-test and the rest of the factors were analyzed by ANOVA; the specific research results are shown in

Table 8. Analysis of variance for demographics.

Item	Category	M ± SD	p	F	LSD
Age	Under 30 years old	32.76 ± 3.592	<0.001	9.838	1 < 2, 3, 4
	31~40 years old	34.82 ± 3.705
	41~50 years old	36.38 ± 4.030
	Over 51 years old	36.11 ± 5.311
Length of service	Less than 10 years	33.22 ± 3.652	<0.001	8.554	2 < 1, 3, 4
	11~20 years	35.10 ± 3.895
	21~30 years	36.65 ± 4.317
	Over 31 years	35.00 ± 4.904
Educational level	Junior high school and below	32.50 ± 2.345	0.003	4.067	2 < 1, 3, 4, 5
	High school, vocational high school, technical secondary school	33.04 ± 2.585
	Junior college	35.63 ± 4.543
	Undergraduate	35.47 ± 4.380
	Graduate and above	34.13 ± 3.563
Accident experience	Yes	30.93 ± 2.173	<0.001	32.470
	No	35.94 ± 4.062

.

The findings of the independent sample t-test and one-way ANOVA test demonstrate that there were statistically significant variations in the levels of psychological resilience among high-place employees with diverse age, length of service, educational level, and accident experience (p < 0.05). Further, we conducted a post hoc analysis of the difference results in the three dimensions of age, length of service, and educational level to determine which category of employees were significantly different compared with other employees. The results show that for the age group, the employees aged less than 30 years were significantly different from the other three age groups, and the mean psychological resilience was significantly lower than that of the other three groups. For the length of service factor, the average psychological resilience of employees in the 11–20 year group was significantly lower than that of employees with other seniority. In terms of educational level, employees with high school, vocational high school, and technical secondary school attainment had significantly less psychological resilience than those with other degrees. The amount of psychological resilience correlated positively with age and length of service, and increased with age and length of service. Among them, workers aged 41~50 had the highest level of psychological resilience. This result is consistent with the trend in depression and anxiety levels decreasing with age in the Chinese National Mental Health Development Report. The level of psychological resilience is positively correlated with educational level and tends to rise as education levels rise; hence, education is a factor in psychological resilience. This result is consistent with the survey study published in the Mental Health Development Report: the higher the degree, the higher the mental health level. Workers with accident experience had considerably lower levels of psychological resilience than those without accident experience, which is detrimental to psychological resilience.

3.4.3. Multiple Linear Regression Analysis

In order to further clarify the effect of differences in demographic characteristics on the level of psychological resilience, the multivariate analysis was conducted by multiple linear regression analysis. A multiple linear regression analysis model was developed using the psychological resilience score as the dependent variable.

The statistical analysis of covariance was performed on the independent variables, and the presence of covariance in the multiple liner regression analysis was tested by means of statistical indicators: tolerance value (TOL) and variance inflation factor (VIF). If there is multivariate collinearity between the respective variables, represented by a TOL value close to 0, or a VIF value greater than 10, the variables are not suitable for multi-element analysis. It was determined that all of the study’s independent variables had TOL values between 0.182 and 0.965, while having VIF values between 1.036 and 5.496. This indicated that the statistical indicators meet the requirements of multi-factor analysis, there was no covariance between the respective variables, and multiple linearity analysis could be performed.

Table 9. Multiple linear regression analysis.

Item	Partial Regression Coefficient	Standard Regression Coefficient	t	p	Coefficient of Determination R2	TOL	VIF
Constant	13.816		8.652	<0.001	0.390
Age	2.164	0.490	4.776	<0.001		0.182	5.496
Length of service	−0.214	−0.048	−0.483	0.629		0.193	5.183
Educational level	1.721	0.336	6.818	<0.001		0.786	1.272
Accident experience	4.990	0.444	9.950	<0.001		0.959	1.403

displays the outcomes of the multiple linear regression analysis.

By establishing a regression model, it can be concluded that age, educational level, and accident experience are the influencing factors on psychological resilience (p < 0.05), which eventually entered the regression equation model. The coefficient of determination, R² = 0.390, indicates that 39.0% of the variance in psychological resilience can be explained by age, educational level, and accident experience.

4. Discussion

The CD-RISC-10 was verified to explore the reliability and validity of the scale among Chinese high-place workers in the construction industry in this study. The discriminative ability of each item on the scale was evaluated. The results show that the correlation coefficients between each item and the total score were all greater than 0.4, and the SMC values met the requirements, indicating that the scale has good discriminative ability. In addition, exploratory factor analysis demonstrated that the CD-RISC-10 is a unidimensional scale with 10 items, and confirmatory factor analysis indicated that all the model fit indices met the requirements. The Cronbach’s α coefficient reached 0.85 in this study, which shows that the scale has high internal consistency. Therefore, the CD-RISC-10 was shown to have good reliability and validity among the high-place workers working high above the ground in China, and it can be used as a valid research tool to measure employees’ levels of mental resilience.

Additionally, the psychological resilience score of 325 workers was (35.07 ± 4.247), slightly higher than the theoretical median, and was at a moderate to high level. The K-means clustering analysis results show that 82.77% of employees have normal psychological resilience, but 17.23% of employees still belong to Cluster 1 (resilience score = 30), which is classified as a group with psychological resilience disorders. Compared with the measurement results of the CD-RISC-10 among other high-risk industries, such as healthcare workers (27.31 ± 6.98) [20] and male military personnel with PTSD (24.85 ± 7.54) and without PTSD (32.36 ± 7.20) [30], the Chinese construction project high-place workers had a higher resilience level. Analyzing the reasons, on the one hand, due to the frequent occurrence of construction accidents, the relevant enterprises have strengthened the relevant skills training and psychological counseling for high-risk positions, so that workers who have been engaged in high-place positions for many years and have certain work experience have their thinking skills enhanced with the increase in operating time, have a reasonable cognitive system for treating their work, and have a correct view of safety, which makes the overall level of the mental toughness of staff higher. On the other hand, some inexperienced young employees, whose thinking skills are lacking, act arbitrarily and have a strong spirit of risk-taking. However, the blind spirit of risk-taking can sometimes blind such operators to overestimate their abilities and still show a high level of psychological resilience. Therefore, enterprises should manage the behavior of such operators to avoid accidents.

By examining the effect of demographic characteristics on psychological resilience, the results of different variables have shown significant differences. This study’s findings on the impact of educational level on psychological toughness are consistent with prior research [31]. It has been noted that there is a strong relationship between educational attainment and workers’ individual learning ability and safety performance [32]. The findings of the present investigation support the notion that having a high degree of education reduces risk when psychological resilience is developing. Through ANOVA, it was found that the psychological resilience score of highly educated employees was significantly higher than that of low-educated employees. The reason may be that employees with higher literacy levels usually have stable jobs and higher incomes and obtain more safety-related information, which is conducive to employees’ correct understanding of hidden dangers, reducing negative emotions and thus resulting in better psychological resilience. Therefore, the education of employees should not be ignored, and enterprise managers should develop corresponding rationalization programs to improve the psychological resilience of inexperienced employees and encourage employees to pursue higher education.

Studies show that workers’ age and length of service positively improve psychological resilience [33], which is compatible with the findings of this study. Age and length of service have a significant difference in the psychological resilience of workers, and the psychological resilience of the elderly should be relatively good. In a study of psychological factors affecting the safety cognition of workers, the age of the workers was divided into older and younger groups, and the hypothesis model of the two age groups was tested using the structural equation model (SEM). The results showed that the older workers showed better psychological qualities when facing challenges and pressures in the workplace, and there were significant differences in safety perception among different ages [34]. There are some similarities between this paper and the research results. The relatively good psychological resilience of older workers is mainly due to their advantages in work experience, emotional regulation, social support, cognition, and learning ability. These advantages enable them to remain calm and rationally respond in the face of work stress and challenges, thus showing a higher level of psychological resilience.

The results of the independent sample t-test results show that the scores of the psychological resilience of employees with different accident experiences were statistically significant (p < 0.05). Accident experience had a detrimental influence on psychological resilience, and employees with accident experience had much poorer psychological resilience than employees without accident experience. Managers should focus on employees with accident experience and provide safety training and psychological guidance to help them recover from their negative psychological state, which can improve the overall level of employees’ psychological resilience to a certain extent. For operators who have experienced accidents, this experience may cause greater psychological stress and trauma, making it more difficult for them to face subsequent challenges [35].

Creating a multiple linear regression analysis model, a multiple factor analysis was performed on the five influencing factors (age, length of service, educational level, and accident experience) that showed statistical significance (p < 0.05) in the single factor analysis findings. Through the establishment of the regression model, it can be concluded that gender, age, educational level, and accident experience are the main influencing factors of psychological resilience (p < 0.05), and 39.0% of psychological resilience differences can be explained by age, education, and accident experience.

This study validated the validity of the CD-RISC-10 and offered a measurement instrument for studies on psychological resilience. It improves the knowledge on the elements affecting workers’ psychological resilience to some level and provides a reference for enterprise managers to develop interventions for employees to cultivate psychological resilience. These are important measures to improve the resilience and proactive safety behavior of high-place construction workers.

Due to limitations in the research time and objective conditions, the coverage of research samples is limited. In the later stage of research, the number of research samples and source areas should be increased to improve the accuracy of the research and make the research results more universal. In further studies, academics should continue to undertake a thorough and in-depth analysis of the aforementioned problems, delve deeper into the variables influencing psychological resilience in a multifaceted and dynamic way, and suggest relevant remedies.

5. Conclusions

This study validated the 10-item Connor–Davidson Resilience Scale (CD-RISC-10), yielding significant insights. The CD-RISC-10 demonstrated a stable, unidimensional structure with robust reliability and validity, making it an effective tool for assessing the psychological resilience of construction workers on elevated platforms. With an average resilience score of 35.07 ± 4.247 for Chinese high-place workers, K-means clustering revealed that while the majority (82.77%) exhibited average to high levels of resilience, a concerning 17.23% scored below the threshold, indicating a need for resilience enhancement strategies. Notably, demographic factors such as age, education, and accident history significantly influence resilience levels, accounting for 39.0% of the variance as identified through. These findings underscore the importance of considering demographic nuances in resilience-building initiatives within the construction industry and other high-risk industries. The sample coverage of this study is limited and it should be followed up by expanding the sample size and exploring multiple aspects. This study enables enterprises to measure the mental health of their workers in a uniform and standardized way, ensuring the objectivity and comparability of the assessment results and reducing the bias caused by subjective judgment. The results of the assessment can help to identify possible mental health risks of workers at an early stage, so that timely interventions can be taken to prevent problems from worsening and to safeguard the workers’ physical and mental health.