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人口统计学因素对 COVID-19 大流行期间建筑工人感知压力源的影响。

Demographic Influences on Perceived Stressors of Construction Workers during the COVID-19 Pandemic.

机构信息

School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China.

出版信息

Int J Environ Res Public Health. 2022 Apr 1;19(7):4192. doi: 10.3390/ijerph19074192.

DOI:10.3390/ijerph19074192
PMID:35409877
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8998247/
Abstract

Construction work is one of the most stressful occupations in the world, and the COVID-19 pandemic has only exacerbated this reality. This research conducted a detailed investigation on the perceived stressors of different demographic groups among construction workers. Empirical data were collected using a structured questionnaire in the Chinese construction industry. The empirical data were processed using both an independent sample -test and an Analysis of Variance (ANOVA). The findings indicated that male workers reported greater workloads than did females. Married workers experienced more pandemic fear and job insecurity, and they were more sensitive to the poor working environment. Highly-educated workers were inclined to be more satisfied with organizational pandemic responses, and had lower job insecurity and role ambiguity, but they experienced heavier workloads. In addition, the differences in work experience and age were statistically significant with regards to job insecurity. This research contributes to the body of knowledge by giving a comprehensive understanding of demographic influences on perceived stressors among construction workers. It also provides valuable insights to identify sensitive demographic groups and promote their health and wellbeing during and after the pandemic.

摘要

建筑工作是世界上压力最大的职业之一,而 COVID-19 大流行只是加剧了这一现实。本研究对建筑工人中不同人口统计学群体的感知压力源进行了详细调查。使用中国建筑行业的结构化问卷收集实证数据。使用独立样本检验和方差分析(ANOVA)对实证数据进行处理。研究结果表明,男性工人报告的工作量比女性大。已婚工人对大流行的恐惧和工作不安全感更大,对恶劣的工作环境更敏感。受教育程度较高的工人更倾向于对组织大流行应对措施感到满意,工作不安全感和角色模糊性较低,但工作量更大。此外,工作经验和年龄的差异在工作不安全感方面具有统计学意义。本研究通过全面了解建筑工人感知压力源的人口统计学影响,为知识体系做出了贡献。它还为识别敏感的人口统计学群体提供了有价值的见解,并在大流行期间和之后促进他们的健康和福祉。

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