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一种视觉工效学与照明条件快速评估方法(RAVEL):深入开发与心理测量学研究

A method for rapid assessment of visual ergonomics and lighting conditions (RAVEL): An in-depth development and psychometrics study.

作者信息

Esmaeili Sayed Vahid, Esmaeili Reza, Shakerian Mahnaz, Dehghan Habibollah, Yazdanirad Saeid, Heidari Zahra, Habibi Ehsanollah

机构信息

Student Research Committee, Department of Occupational Health and Safety Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.

Student Research Committee, Department of Occupational Health and Safety Engineering, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

出版信息

Work. 2025 Jan;80(1):441-460. doi: 10.3233/WOR-240052. Epub 2025 Mar 18.

Abstract

BACKGROUND

In workplaces heavily reliant on visual tasks, various factors can significantly influence an individual's performance, necessitating the use of reliable tools to identify and mitigate these factors.

OBJECTIVE

This study aimed to develop a swift assessment method for visual ergonomics and lighting conditions, evaluating its validity in real-world scenarios.

METHODS

The questionnaire's content validity was determined by a panel of experts using the content validity ratio (CVR) and content validity index (CVI). Construct validity was assessed through exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and latent class analysis (LCA). Internal consistency was measured using Cronbach's alpha coefficient. The RAVEL index, derived from the calculated effect coefficients of items, classified total scores through receiver operator curves (ROCs).

RESULTS

The rapid assessment method, comprising two parts with 30 items, demonstrated acceptable reliability with CVR, CVI, and Cronbach's alpha coefficient () at 0.75, 0.87, and 0.896, respectively. The EFA on the first part's 22 items identified three factors, confirmed by CFA. The LCA on the second part's eight items revealed that a two-class model best fit the data, with Bayesian information criterion (BIC) = 24249, 17, Akaik information criterion (AIC) = 2179.89, and an entropy R-squared of 0.83, indicating appropriate subject classification based on the model. The RAVEL score was categorized into three levels, with optimal cut points of 55 and 63.

CONCLUSIONS

In conclusion, the study demonstrated that this method based on visual ergonomics serves as a rapid and reliable tool for assessing visual ergonomic risks of display users in the workplace.

摘要

背景

在严重依赖视觉任务的工作场所,各种因素会显著影响个人绩效,因此需要使用可靠的工具来识别和减轻这些因素。

目的

本研究旨在开发一种用于视觉工效学和照明条件的快速评估方法,并在实际场景中评估其有效性。

方法

通过专家小组使用内容效度比(CVR)和内容效度指数(CVI)来确定问卷的内容效度。通过探索性因素分析(EFA)、验证性因素分析(CFA)和潜在类别分析(LCA)评估结构效度。使用克朗巴哈α系数测量内部一致性。从项目的计算效应系数得出的RAVEL指数通过接收者操作特征曲线(ROC)对总分进行分类。

结果

快速评估方法由两部分共30个项目组成,CVR、CVI和克朗巴哈α系数分别为0.75、0.87和0.896,显示出可接受的可靠性。对第一部分的22个项目进行的EFA识别出三个因素,并由CFA确认。对第二部分的8个项目进行的LCA表明,两类模型最适合数据,贝叶斯信息准则(BIC)=24249.17,赤池信息准则(AIC)=2179.89,熵R平方为0.83,表明基于该模型的受试者分类合适。RAVEL分数分为三个级别,最佳切点为55和63。

结论

总之,该研究表明,这种基于视觉工效学的方法可作为一种快速可靠的工具,用于评估工作场所显示器用户的视觉工效学风险。

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