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      Hazard Recognition Patterns Demonstrated by Construction Workers

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          Abstract

          Construction workers fail to recognize a large number of safety hazards. These unrecognized safety hazards can lead to unintended hazard exposure and tragic safety incidents. Unfortunately, traditional hazard recognition interventions (e.g., job hazard analyses and safety training) have been unable to tackle the industry-wide problem of poor hazard recognition levels. In fact, emerging evidence has demonstrated that traditional hazard recognition interventions have been designed without a proper understanding of the challenges workers experience during hazard recognition efforts. Interventions and industry-wide efforts designed based on a more thorough understanding of these challenges can yield substantial benefits—including superior hazard recognition levels and lower injury rates. Towards achieving this goal, the current investigation focused on identifying hazard categories that workers are more proficient in recognizing and others that they are less proficient in recognizing (i.e., hazard recognition patterns). For the purpose of the current study, hazards were classified on the basis of the energy source per Haddon’s energy release theory (e.g., gravity, motion, electrical, chemical, etc.). As part of the study, 287 workers representing 57 construction workplaces in the United States were engaged in a hazard recognition activity. Apart from confirming previous research findings that workers fail to recognize a disproportionate number of safety hazards, the results demonstrate that the workers are more proficient in recognizing certain hazard types. More specifically, the workers on average recognized roughly 47% of the safety hazards in the gravity, electrical, motion, and temperature hazard categories while only recognizing less than 10% of the hazards in the pressure, chemical, and radiation hazard categories. These findings can inform the development of more robust interventions and industry-wide initiatives to tackle the issue of poor hazard recognition levels in the construction industry.

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          Normality Tests for Statistical Analysis: A Guide for Non-Statisticians

          Statistical errors are common in scientific literature and about 50% of the published articles have at least one error. The assumption of normality needs to be checked for many statistical procedures, namely parametric tests, because their validity depends on it. The aim of this commentary is to overview checking for normality in statistical analysis using SPSS.
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                Author and article information

                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                24 October 2020
                November 2020
                : 17
                : 21
                : 7788
                Affiliations
                [1 ]Department of Civil, Construction, and Environmental Engineering, North Carolina State University, 2501 Stinson Dr., Raleigh, NC 27607, USA; alex_albert@ 123456ncsu.edu (A.A.); afalshar@ 123456ncsu.edu (A.A.); bkpandit@ 123456ncsu.edu (B.P.); yspatil@ 123456ncsu.edu (Y.P.)
                [2 ]Department of Civil, Construction, and Environmental Engineering, The University of Alabama, 3023 HM Comer, Tuscaloosa, AL 35487, USA; cnnaji@ 123456eng.ua.edu
                Author notes
                [* ]Correspondence: suddin@ 123456ncsu.edu ; Tel.: +1-786-660-8824
                Author information
                https://orcid.org/0000-0002-9885-9504
                https://orcid.org/0000-0002-3725-4376
                Article
                ijerph-17-07788
                10.3390/ijerph17217788
                7663096
                33114347
                256f36f6-53b3-47a3-9f54-e0312e8f139b
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 29 September 2020
                : 22 October 2020
                Categories
                Article

                Public health
                construction safety,hazard recognition,occupational safety,worker safety,hazard recognition pattern,construction hazards,safety risks

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