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DEVELOPMENT OF A PREDICTIVE MODEL FOR WORK RELATEDNESS OF MSDS AMONG SEMICONDUCTOR BACK-END WORKERS

Objective. Limited models are available to predict work-relatedness of musculoskeletal disorders (MSDs) among semiconductor back-end workers. This study aims to develop a model to predict the MSDs development among back-end workers. Method. Potential MSD risk factors were extracted from 277 work com...

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Main Authors: LING, CHAI FONG, Radin Umar, Zaid @ Radin Zaid, Ahmad, Nadiah
Format: Article
Language:English
Published: Taylor and Francis Ltd. 2022
Online Access:http://eprints.utem.edu.my/id/eprint/27013/2/0128919062023233.PDF
http://eprints.utem.edu.my/id/eprint/27013/
https://www.tandfonline.com/doi/full/10.1080/10803548.2020.1840116
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spelling my.utem.eprints.270132023-12-14T12:11:04Z http://eprints.utem.edu.my/id/eprint/27013/ DEVELOPMENT OF A PREDICTIVE MODEL FOR WORK RELATEDNESS OF MSDS AMONG SEMICONDUCTOR BACK-END WORKERS LING, CHAI FONG Radin Umar, Zaid @ Radin Zaid Ahmad, Nadiah Objective. Limited models are available to predict work-relatedness of musculoskeletal disorders (MSDs) among semiconductor back-end workers. This study aims to develop a model to predict the MSDs development among back-end workers. Method. Potential MSD risk factors were extracted from 277 work compensation investigation reports conducted between 2011-2019. Binary logistic regression approach was used to determine significant predictors. Results. Significant predictors (p < 0.05) include poor posture (odds ratio [OR] = 1.822; 95% confidence interval [CI] [1.261, 2.632]), forceful exertion (OR = 1.741; 95% CI [1.281, 2.367]), static posture (OR = 1.796; 95% CI [1.367, 2.378]), lifting and lowering (OR = 1.438; 95% CI [0.966, 1.880]), transferring (OR = 1.533; 95% CI [1.101, 2.136]), pushing and pulling (OR = 0.990; 95% CI [0.744, 1.317]), repairing machines (OR = 0.845; 95% CI 76 [0.616, 1.159]), preventive maintenance (OR = 1.061; 95% CI [0.765, 1.471]) and quality inspection (OR = 0.982; 95% CI [0.729, 1.322]). Confounding factors and employment duration played crucial roles in the model. Cross-validation of predictive model was 86.2%, while face validation among 30 experts was 7.9/10 (SD 1.9). Conclusion. The model allows practitioners to predict potential MSD cases among semiconductor back-end workers and proactively plan appropriate mitigation measures. Taylor and Francis Ltd. 2022-04 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/27013/2/0128919062023233.PDF LING, CHAI FONG and Radin Umar, Zaid @ Radin Zaid and Ahmad, Nadiah (2022) DEVELOPMENT OF A PREDICTIVE MODEL FOR WORK RELATEDNESS OF MSDS AMONG SEMICONDUCTOR BACK-END WORKERS. INTERNATIONAL JOURNAL OF OCCUPATIONAL SAFETY ANDERGONOMICS, 28 (2). pp. 872-882. ISSN 1080-3548 https://www.tandfonline.com/doi/full/10.1080/10803548.2020.1840116 10.1080/10803548.2020.1840116
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description Objective. Limited models are available to predict work-relatedness of musculoskeletal disorders (MSDs) among semiconductor back-end workers. This study aims to develop a model to predict the MSDs development among back-end workers. Method. Potential MSD risk factors were extracted from 277 work compensation investigation reports conducted between 2011-2019. Binary logistic regression approach was used to determine significant predictors. Results. Significant predictors (p < 0.05) include poor posture (odds ratio [OR] = 1.822; 95% confidence interval [CI] [1.261, 2.632]), forceful exertion (OR = 1.741; 95% CI [1.281, 2.367]), static posture (OR = 1.796; 95% CI [1.367, 2.378]), lifting and lowering (OR = 1.438; 95% CI [0.966, 1.880]), transferring (OR = 1.533; 95% CI [1.101, 2.136]), pushing and pulling (OR = 0.990; 95% CI [0.744, 1.317]), repairing machines (OR = 0.845; 95% CI 76 [0.616, 1.159]), preventive maintenance (OR = 1.061; 95% CI [0.765, 1.471]) and quality inspection (OR = 0.982; 95% CI [0.729, 1.322]). Confounding factors and employment duration played crucial roles in the model. Cross-validation of predictive model was 86.2%, while face validation among 30 experts was 7.9/10 (SD 1.9). Conclusion. The model allows practitioners to predict potential MSD cases among semiconductor back-end workers and proactively plan appropriate mitigation measures.
format Article
author LING, CHAI FONG
Radin Umar, Zaid @ Radin Zaid
Ahmad, Nadiah
spellingShingle LING, CHAI FONG
Radin Umar, Zaid @ Radin Zaid
Ahmad, Nadiah
DEVELOPMENT OF A PREDICTIVE MODEL FOR WORK RELATEDNESS OF MSDS AMONG SEMICONDUCTOR BACK-END WORKERS
author_facet LING, CHAI FONG
Radin Umar, Zaid @ Radin Zaid
Ahmad, Nadiah
author_sort LING, CHAI FONG
title DEVELOPMENT OF A PREDICTIVE MODEL FOR WORK RELATEDNESS OF MSDS AMONG SEMICONDUCTOR BACK-END WORKERS
title_short DEVELOPMENT OF A PREDICTIVE MODEL FOR WORK RELATEDNESS OF MSDS AMONG SEMICONDUCTOR BACK-END WORKERS
title_full DEVELOPMENT OF A PREDICTIVE MODEL FOR WORK RELATEDNESS OF MSDS AMONG SEMICONDUCTOR BACK-END WORKERS
title_fullStr DEVELOPMENT OF A PREDICTIVE MODEL FOR WORK RELATEDNESS OF MSDS AMONG SEMICONDUCTOR BACK-END WORKERS
title_full_unstemmed DEVELOPMENT OF A PREDICTIVE MODEL FOR WORK RELATEDNESS OF MSDS AMONG SEMICONDUCTOR BACK-END WORKERS
title_sort development of a predictive model for work relatedness of msds among semiconductor back-end workers
publisher Taylor and Francis Ltd.
publishDate 2022
url http://eprints.utem.edu.my/id/eprint/27013/2/0128919062023233.PDF
http://eprints.utem.edu.my/id/eprint/27013/
https://www.tandfonline.com/doi/full/10.1080/10803548.2020.1840116
_version_ 1787140267340464128
score 13.211869

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