Evidence map›Paper›PMID 39687112›Full record

ArticleHeliyon2024

Research on the mechanism of human-machine security collaboration of miners considering automation trust.

Juan Yang, Xue Yang, Shan Chai, Likun Ni, Xiao Wang, Langxuan Pan

Abstract read
In one paragraph

Article in Heliyon, 2024. 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

6 authors.

Juan YangSchool of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou, China.
Xue YangSchool of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou, China.
Shan ChaiSchool of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou, China.
Likun NiSchool of Management, Henan University of Urban Construction, Pingdingshan, China.
Xiao WangSchool of Economics and Management, Zhongyuan University of Technology, Zhengzhou, China.
Langxuan PanSchool of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Safety in the coal mining industry is a long-standing issue. With the implementation of intelligent construction in coal mines, humans-machine collaboration is critical to accident prevention. We investigate the psychological dynamics among miners during intelligent construction by developing distinct evolutionary game models for miner security collaboration mechanisms, while also considering the level of trust in automation. We explore the impact of changes in automation trust on the human-machine security collaboration behaviour among miners as well as the regulatory strategies adopted by coal mining enterprises. Evidently, the current level of automation trust among miners is insufficient to ensure safety, and the safety management systems of coal mining enterprises are yet to reach a stable state. Examination of the relevant parameters using specific examples reveals that the optimal range for the level of automation trust among miners lies between 0.7 and 0.9, indicating a favourable coal mine safety production system. These conclusions provide a scientific foundation for effectively enhancing safety management in the context of intelligent construction in coal mines.

Indexed as

Automated trustEvolutionary gameIntelligent coal mine constructionSafety supervisionSecurity collaboration behaviour

Identifiers

PMID39687112
PMCPMC11648194

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.