ArticleInternational journal of environmental research and public health2019
Using Improved Principal Component Analysis to Explore Construction Accident Situations from the Multi-Dimensional Perspective: A Chinese Study.
Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed.
- Exploring the economic occupational health, safety, and fatal accidents in high-risk industries.BMC public health · 2025Article
- Construction safety: an analysis of the cross-influence of economic, construction, and accident death factors.Environmental science and pollution research international · 2021Article
- Analysis of the Characteristics of Fatal Accidents in the Construction Industry in China Based on Statistical Data.International journal of environmental research and public health · 2021Article
- Feasibility of Casein to Record Stable Isotopic Variation of Cow Milk in New Zealand.Molecules (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The improvement of the macro-level accident situation in the Chinese construction industry is currently an urgent task for the government due to the high accident rate. This study intends to use improved principal component analysis to explore the accident situations in the Chinese construction industry from a multi-dimensional perspective, aiming at providing targeted direction on the improvement of the accident situation for the government. Six composite indicators that can quantify the accident situation are firstly selected based on a wide review of the literature and interviews with safety experts, with the original data collected from China institutions. The classical principal component analysis is then improved to examine the correlations between indicators, and further to evaluate accident situations in China provinces. Finally, the features of accident situations are explored and analyzed from a multi-dimensional perspective. The findings show that the improved principal component analysis can retain more dispersion degree information of the original data. Meanwhile, three principal components including the accident frequency, trend, and severity were extracted to quantify the accident situation, and a hierarchical indicator system for the comprehensive evaluation of the accident situation was constructed to deeper understand multi-dimensional characteristics of China's accident situations. Furthermore, there exist great regional differences of accident situations in Chinese provinces. From the overall perspective, the accident situation shows a declining trend from the western backward region to the highly developed eastern coastal region. This study provides a multi-dimensional perspective for the government to formulate safety regulations and improve the accident situation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.