Evidence map›Paper›PMID 33012750›Full record

ArticleIndustrial health2021

Early intervention mechanism for preventing electrocution in construction engineering.

Yau-Ren Shiau, Fang-Yi Lo, Po-Cheng Ko

Open access · diamondAbstract read
In one paragraph

Article in Industrial health, 2021. 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
0.4field-weighted citation impact, top 36% 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, 8 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

3 authors at 1 institution in 1 country.

Yau-Ren ShiauDepartment of Industrial Engineering and Systems Management, Feng Chia University, Taiwan, R.O.C.
Fang-Yi LoDepartment of International Business, Feng Chia University, Taiwan, R.O.C.
Po-Cheng KoPhD Program of Business, Feng Chia University, Taiwan, R.O.C.
Feng Chia University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of this study is to establish an effective early intervention mechanism for construction engineering to prevent electrocution while improving labor safety and reducing the casualty risk. This study used narrative text analysis and the Haddon Matrix for data collection, and analyzed the causes from the 113 electrocution deaths among in the construction industry, the exhaustive chi-square automatic interaction detector algorithm was employed the segmentation of the correlations. Based on the theory of inventive problem solving, through IDEF0 (ICAM DEFinition) for function modeling was designed the early intervention mechanism. This study revealed the operating features related to electric shock hazards. Early intervention was introduced to reduce the relevant risks and establish safety mechanisms. The first contribution of this study is the determination of hazard correlations between operating features and conductive media, and entry point for the prevention of electrocutions. The second contribution is the suggestion of the establishment of inspection stations for electric tools, thereby ensuring that the portable power tools are safe. The final contribution is the joint application of TRIZ (Teoriya Resheniya Izobreatatelskikh Zadatch) and IDEF0, which establishing the pre-entry testing, strengthening safety mechanisms.

Indexed as

Accidents, OccupationalConstruction IndustryElectrical Equipment and SuppliesElectric InjuriesHumansRisk FactorsEarly intervention mechanismElectrocutionInspection stations for electric toolsPortable electrical toolsPrevention through design

Identifiers

PMID33012750
PMCPMC7855676
OpenAlexW3090379618

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.