OBJECTIVES: Microelectronic engineers are considered valuable human capital contributing significantly toward economic development, but they may encounter stressful work conditions in the context of a globalized industry. The study aims at identifying risk factors of depressive disorders primarily based on job stress models, the Demand-Control-Support and Effort-Reward Imbalance models, and at evaluating whether depressive disorders impair work performance in microelectronics engineers in Taiwan. METHODS: The case-control study was conducted among 678 microelectronics engineers, 452 controls and 226 cases with depressive disorders which were defined by a score 17 or more on the Beck Depression Inventory and a psychiatrist's diagnosis. The self-administered questionnaires included the Job Content Questionnaire, Effort-Reward Imbalance Questionnaire, demography, psychosocial factors, health behaviors and work performance. Hierarchical logistic regression was applied to identify risk factors of depressive disorders. Multivariate linear regressions were used to determine factors affecting work performance. RESULTS: By hierarchical logistic regression, risk factors of depressive disorders are high demands, low work social support, high effort/reward ratio and low frequency of physical exercise. Combining the two job stress models may have better predictive power for depressive disorders than adopting either model alone. Three multivariate linear regressions provide similar results indicating that depressive disorders are associated with impaired work performance in terms of absence, role limitation and social functioning limitation. CONCLUSIONS: The results may provide insight into the applicability of job stress models in a globalized high-tech industry considerably focused in non-Western countries, and the design of workplace preventive strategies for depressive disorders in Asian electronics engineering population.
OBJECTIVES: Microelectronic engineers are considered valuable human capital contributing significantly toward economic development, but they may encounter stressful work conditions in the context of a globalized industry. The study aims at identifying risk factors of depressive disorders primarily based on job stress models, the Demand-Control-Support and Effort-Reward Imbalance models, and at evaluating whether depressive disorders impair work performance in microelectronics engineers in Taiwan. METHODS: The case-control study was conducted among 678 microelectronics engineers, 452 controls and 226 cases with depressive disorders which were defined by a score 17 or more on the Beck Depression Inventory and a psychiatrist's diagnosis. The self-administered questionnaires included the Job Content Questionnaire, Effort-Reward Imbalance Questionnaire, demography, psychosocial factors, health behaviors and work performance. Hierarchical logistic regression was applied to identify risk factors of depressive disorders. Multivariate linear regressions were used to determine factors affecting work performance. RESULTS: By hierarchical logistic regression, risk factors of depressive disorders are high demands, low work social support, high effort/reward ratio and low frequency of physical exercise. Combining the two job stress models may have better predictive power for depressive disorders than adopting either model alone. Three multivariate linear regressions provide similar results indicating that depressive disorders are associated with impaired work performance in terms of absence, role limitation and social functioning limitation. CONCLUSIONS: The results may provide insight into the applicability of job stress models in a globalized high-tech industry considerably focused in non-Western countries, and the design of workplace preventive strategies for depressive disorders in Asian electronics engineering population.
Authors: Bo Netterstrøm; Nicole Conrad; Per Bech; Per Fink; Ole Olsen; Reiner Rugulies; Stephen Stansfeld Journal: Epidemiol Rev Date: 2008-06-27 Impact factor: 6.222
Authors: Ronald Kessler; Leigh Ann White; Howard Birnbaum; Ying Qiu; Yohanne Kidolezi; David Mallett; Ralph Swindle Journal: J Occup Environ Med Date: 2008-07 Impact factor: 2.162
Authors: David A Adler; Thomas J McLaughlin; William H Rogers; Hong Chang; Leueen Lapitsky; Debra Lerner Journal: Am J Psychiatry Date: 2006-09 Impact factor: 18.112
Authors: Hynek Pikhart; Martin Bobak; Andrzej Pajak; Sofia Malyutina; Ruzena Kubinova; Roman Topor; Helena Sebakova; Yuri Nikitin; Michael Marmot Journal: Soc Sci Med Date: 2004-04 Impact factor: 4.634
Authors: Wei-Lieh Huang; Yue Leon Guo; Pau-Chung Chen; Jui Wang; Po-Ching Chu Journal: Int J Environ Res Public Health Date: 2017-09-19 Impact factor: 3.390
Authors: Suzanne Gm van Hees; Bouwine E Carlier; Emma Vossen; Roland Wb Blonk; Shirley Oomens Journal: Scand J Work Environ Health Date: 2021-12-08 Impact factor: 5.492
Authors: Chao Wang; Shuang Li; Tao Li; Shanfa Yu; Junming Dai; Xiaoman Liu; Xiaojun Zhu; Yuqing Ji; Jin Wang Journal: Int J Environ Res Public Health Date: 2016-08-12 Impact factor: 3.390