Evidence map›Paper›PMID 37089282›Full record

ArticleHeliyon2023

Causal analysis of coach and bus accidents in China based on road alignments.

Qingxia Zhang, Yao Lu, Fan Feng, Junyan Hu

Abstract read
In one paragraph

Article in Heliyon, 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

4 authors.

Qingxia ZhangSchool of Public Management, Gansu University of Political Science and Law, Lanzhou, China.
Yao LuSchool of Management, Lanzhou University, Lanzhou, China.
Fan FengSchool of Foreign Languages, Shenyang Normal University, Shenyang, China.
Junyan HuCollege of Social Development and Public Administration, Northwest Normal University, Lanzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Given the complexity and the difficulty of controlling contributors effectively, road passenger transport often results in serious injuries and fatalities. The purpose of this study is to identify the main contributors to coach and bus accidents and to provide policy recommendations for making improvements in accident prevention. The Driving Reliability and Error Analysis Method 3.0 (DREAM 3.0) was modified and used to analyze the contributing factors (i.e. phenotypes and genotypes in DREAM) and their casual mechanisms. By having statistical analysis and social network analysis (SNA) adopted, the main genotypes and phenotypes of the DREAM charts were identified. The results of the study showed that A2.1 (too high speed) was the key phenotype and the main genotypic process chain leading to the phenotype was "inadequate safety management → inadequate training → inadequate skills/knowledge → misjudgment of the situation → too high speed" on all types of road. For A2.1 (too high speed), C2 (misjudgment of the situation) was the dominant genotype, while N5 (inadequate safety management) was the root cause of most genotypes. This suggests that road passenger transport companies, as the responsible parties, often fail to implement or violate safety prevention and control systems. Government regulators should promote the policy system and incentivize them to fulfil their safety management responsibilities. The government should also educate the public and improve the road environment to reduce passenger-related risks and the impact of environmental factors on drivers.

Indexed as

Causal mechanismCoach and bus accidentGenotypeRoad alignmentSpecific phenotype

Identifiers

PMID37089282
PMCPMC10114227

What OpenQuestion holds

Textmetadata
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.