Evidence map›Paper›PMID 37662085›Full record

ArticleComputational intelligence and neuroscience2023

Evaluation and Improvement of Employee Performance with respect to Health, Safety, and Environment (HSE) Factors: A Case of Complex Transport Construction Project.

Xinying Zhang, Yuanqing Wang, Yanan Gao, Yuanyuan Liu, Shuo Feng

Abstract read
In one paragraph

Article in Computational intelligence and neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Xinying ZhangCollege of Transportation Engineering, Department of Traffic Engineering, Chang'an University, Xi'an 710064, China.ORCID https://orcid.org/0009-0007-7502-230X
Yuanqing WangCollege of Transportation Engineering, Department of Traffic Engineering, Chang'an University, Xi'an 710064, China.ORCID https://orcid.org/0000-0002-3742-6588
Yanan GaoCollege of Transportation Engineering, Department of Traffic Engineering, Chang'an University, Xi'an 710064, China.ORCID https://orcid.org/0000-0002-1124-9464
Yuanyuan LiuGuangdong University of Technology, Guangzhou 510006, Guangdong, China.ORCID https://orcid.org/0000-0001-5155-4992
Shuo FengHebei Provincial Communications Planning and Design Institute, Shijiazhuang 050021, Hebei, China.ORCID https://orcid.org/0009-0007-7511-9929

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Risk control in complex transport construction is complicated due to the dangerous nature of high variation and unpredictability. Most of the current research analysis focuses on the health, safety, and environment (HSE) risk assessment and employee performance evaluation, which neglects the impact of HSE risks on employee performance. Consequently, this research develops a framework to evaluate employee performance and identify key factors affecting performance. The employee performance indicators and HSE indicators are established by reviewing related literature. Using data from questionnaires, an artificial neural network- (ANN-) based model of employee activity effectiveness is then developed to evaluate employee performance. Sensitivity analysis is implemented to determine the key factors affecting employee performance. The Hong Kong-Zhuhai-Macau Bridge, a large-scale cross-sea channel project, is taken as a case study for validation. The model results show that the employees are satisfied with the effect of HSE management in general, but the psychological stress they perceive becomes large. The indicators of risk control and employee participation positively impact employee performance, while job satisfaction has a negative impact on performance. These findings indicate that operators should pay more attention to employees' psychological perception of work and form a standardized process management and control plan to prevent cumbersome processes from increasing employees' workload. This study helps construction systems and managers to identify the areas of strengths and weaknesses in their HSE management. The research only focuses on the impact of HSE risks on managers' performance in the complex transport construction project. In the future, further engineering projects and employee performance in composite scenarios can be investigated to improve the overall productivity.

Indexed as

EngineeringNeural Networks, ComputerHong KongHumansMacauRisk Assessment

Identifiers

PMID37662085
PMCPMC10474964

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.