Saeid Yazdanirad, G. Pourtaghi, M. Raei, M. Ghasemi
{"title":"钢铁行业员工肌肉骨骼疾病(CRAMUD)综合风险评估工具的开发和验证","authors":"Saeid Yazdanirad, G. Pourtaghi, M. Raei, M. Ghasemi","doi":"10.1080/1463922X.2022.2086643","DOIUrl":null,"url":null,"abstract":"Abstract This study aimed to develop and validate a tool for the comprehensive risk assessment of musculoskeletal disorders (CRAMUD). In this cross-sectional study, 300 male workers participated. Data related to personal, psychosocial and physical items and musculoskeletal symptoms were gathered by a designed questionnaire and Cornell musculoskeletal discomfort questionnaire (CMDQ), respectively. Then, the effect coefficients of the items were computed for developing the CRAMUD equation. The total score of the CRAMUD tool was classified by receiver operator curves (ROCs), and it was validated by linear regression analysis. The values of the content validity ratio (CVR), content validity index (CVI) and Cronbach’s coefficient alpha (α) of the CRAMUD questionnaire with 38 items were calculated as 0.773, 0.934 and 0.940, respectively. The personal, psychosocial and physical items with the coefficients of 0.265, 0.175 and 0.478 had significant effects on the occurrence of musculoskeletal symptoms, respectively. The equation of the novel tool was written by these coefficients. The CRAMUD score was grouped into four levels by optimal cut-off points of 8.51, 11.03 and 15.31. This tool could predict 75% of variations of musculoskeletal symptoms. This tool can be exploited to accurately estimate the risk level of musculoskeletal symptoms in various jobs.","PeriodicalId":22852,"journal":{"name":"Theoretical Issues in Ergonomics Science","volume":"24 1","pages":"335 - 358"},"PeriodicalIF":1.4000,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development and validation of a tool for the comprehensive risk assessment of musculoskeletal disorders (CRAMUD) among employees of a steel industry\",\"authors\":\"Saeid Yazdanirad, G. Pourtaghi, M. Raei, M. Ghasemi\",\"doi\":\"10.1080/1463922X.2022.2086643\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract This study aimed to develop and validate a tool for the comprehensive risk assessment of musculoskeletal disorders (CRAMUD). In this cross-sectional study, 300 male workers participated. Data related to personal, psychosocial and physical items and musculoskeletal symptoms were gathered by a designed questionnaire and Cornell musculoskeletal discomfort questionnaire (CMDQ), respectively. Then, the effect coefficients of the items were computed for developing the CRAMUD equation. The total score of the CRAMUD tool was classified by receiver operator curves (ROCs), and it was validated by linear regression analysis. The values of the content validity ratio (CVR), content validity index (CVI) and Cronbach’s coefficient alpha (α) of the CRAMUD questionnaire with 38 items were calculated as 0.773, 0.934 and 0.940, respectively. The personal, psychosocial and physical items with the coefficients of 0.265, 0.175 and 0.478 had significant effects on the occurrence of musculoskeletal symptoms, respectively. The equation of the novel tool was written by these coefficients. The CRAMUD score was grouped into four levels by optimal cut-off points of 8.51, 11.03 and 15.31. This tool could predict 75% of variations of musculoskeletal symptoms. This tool can be exploited to accurately estimate the risk level of musculoskeletal symptoms in various jobs.\",\"PeriodicalId\":22852,\"journal\":{\"name\":\"Theoretical Issues in Ergonomics Science\",\"volume\":\"24 1\",\"pages\":\"335 - 358\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2022-06-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Theoretical Issues in Ergonomics Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/1463922X.2022.2086643\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ERGONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Theoretical Issues in Ergonomics Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/1463922X.2022.2086643","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ERGONOMICS","Score":null,"Total":0}
Development and validation of a tool for the comprehensive risk assessment of musculoskeletal disorders (CRAMUD) among employees of a steel industry
Abstract This study aimed to develop and validate a tool for the comprehensive risk assessment of musculoskeletal disorders (CRAMUD). In this cross-sectional study, 300 male workers participated. Data related to personal, psychosocial and physical items and musculoskeletal symptoms were gathered by a designed questionnaire and Cornell musculoskeletal discomfort questionnaire (CMDQ), respectively. Then, the effect coefficients of the items were computed for developing the CRAMUD equation. The total score of the CRAMUD tool was classified by receiver operator curves (ROCs), and it was validated by linear regression analysis. The values of the content validity ratio (CVR), content validity index (CVI) and Cronbach’s coefficient alpha (α) of the CRAMUD questionnaire with 38 items were calculated as 0.773, 0.934 and 0.940, respectively. The personal, psychosocial and physical items with the coefficients of 0.265, 0.175 and 0.478 had significant effects on the occurrence of musculoskeletal symptoms, respectively. The equation of the novel tool was written by these coefficients. The CRAMUD score was grouped into four levels by optimal cut-off points of 8.51, 11.03 and 15.31. This tool could predict 75% of variations of musculoskeletal symptoms. This tool can be exploited to accurately estimate the risk level of musculoskeletal symptoms in various jobs.