Evidence map›Paper›PMID 36293694›Full record

ArticleInternational journal of environmental research and public health2022

Optimization of Work Environment and Community Labor Health Based on Digital Model-Empirical Evidence from Developing Countries.

Shiya Gao, Zeyu Wang, Shaoxiang Jiang, Wen Ding, Yuchen Wang, Xiufang Dong

Open access · goldAbstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. 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
0.3field-weighted citation impact, top 37% of its field
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, 2 citations in OpenAlex.

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 at 5 institutions in 3 countries.

Shiya GaoSchool of Management, Wuhan Polytechnic University, Wuhan 430023, China.
Zeyu WangSchool of Public Administration, Guangzhou University, Guangzhou 510006, China.
Shaoxiang JiangNational School of Development, Peking University, Beijing 100871, China.
Wen DingFaculty of Engineering, University of Waterloo, Waterloo, ON N2L 6J5, Canada.
Yuchen WangCarey School of Business, Johns Hopkins University, Baltimore, MD 21202, USA.ORCID 0000-0002-3643-6127
Xiufang DongSchool of Public Administration, Guangzhou University, Guangzhou 510006, China.
Guangzhou University · CNJohns Hopkins University · USPeking University · CNUniversity of Waterloo · CAWuhan Polytechnic University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As far as we know, for large manufacturing enterprises, there is often a community of labor gathered around such enterprises, which is especially used as a place for the enterprise to place the labor force. This paper aimed to update the industry model of Chinese Manufacturing Enterprises (CMEs) to improve workers' health management. This work first discusses the value, mode, and process of Enterprise Digital Transformation (EDT) and Worker Health and Safety Management (WHSM). Then, it proposes the CMEs-oriented EDT model and WHSM system based on Big Data Technology (BDT) and the Internet of Things (IoT). The proposed model and system are verified through a case study on the Shanghai BYD manufacturing enterprise (short for BYD) using the Fuzzy Comprehensive Evaluation Method (CFEM). The EDT model verification considers the adaptation and performance of enterprises after EDT. The WHSM system considers workers' oxygen inhalation status to evaluate their heart and cardiovascular health. The results show that EDT improves the enterprise's revenue and reshuffles the revenue structure. The EDT model has absolute adaptability to BYD. It has greatly improved BYD's indexes, especially financial performance, market capability, and technical capability.

Indexed as

Developing CountriesOccupational HealthChinaHumansOxygenWorkplaceOxygenbig data technologyenterprise digital transformationFCEMFCEM manufacturing enterpriseshealth and safety managementinternet of things

Identifiers

PMID36293694
PMCPMC9603667
OpenAlexW4304806953

What OpenQuestion holds

Textmetadata
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.