Evidence map›Paper›PMID 38163236›Full record

ArticleHeliyon2024

Comprehensive risk management of health, safety and environment for social emergency rescue organization.

Mengyao Wang, Haoying Zhang, Yun Luo, Ming Xu

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

4 authors.

Mengyao WangCollege of Engineering and Technology, China University of Geoscience (Beijing), Beijing, 100083, China.
Haoying ZhangCollege of Engineering and Technology, China University of Geoscience (Beijing), Beijing, 100083, China.
Yun LuoCollege of Engineering and Technology, China University of Geoscience (Beijing), Beijing, 100083, China.
Ming XuCollege of Engineering and Technology, China University of Geoscience (Beijing), Beijing, 100083, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

During the rescue and relief work of social emergency rescue organizations, the rescue team members face significant responsibilities and risks. Social rescue organizations need to improve funding, available equipment and other aspects compared with professional emergency rescue organizations. Moreover, the development of rescue levels among emergency rescue organizations is unbalanced, and rescue teams'' comprehensive quality and skills are uneven. To understand the safety risks of these organizations before the implementation of rescue and relief tasks, the task situation and its characteristics must be assessed timely, and safety must be ensured under the premise of efficient completion of the rescue missions. Based on the theory of safety system engineering and health, safety, and environment risk management, a risk management model is established to achieve a closed-loop risk management. The risk factors in rescue and relief tasks of social rescue organizations were identified, and a health, safety and environment risk assessment index system and grading standard were established. A gray cloud model was applied for the evaluation method, the problems of information randomness, risk-level boundary fuzziness and randomness of the evaluation index data were effectively solved. Subsequently, a risk hierarchical early warning and control strategies were proposed to allocate emergency resources rationally. The proposed method was verified and found to have universal applicability and strong practicability.

Indexed as

Gray cloud modelHealthRescue and reliefRisk managementSafety and environmentSocial emergency rescue organization

Identifiers

PMID38163236
PMCPMC10754858

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.