Anne M Gadermann1, Steven G Heeringa2, Murray B Stein3, Robert J Ursano4, Lisa J Colpe5, Carol S Fullerton4, Stephen E Gilman6, Michael J Gruber7, Matthew K Nock8, Anthony J Rosellini7, Nancy A Sampson7, Michael Schoenbaum9, Alan M Zaslavsky7, Ronald C Kessler7. 1. Centre for Health Evaluation and Outcome Sciences, St. Paul's Hospital, University of British Columbia, 620B-1081 Burrard Street, Vancouver, BC V6Z 1Y6. 2. Institute for Social Research, University of Michigan, 426 Thompson Street, Ann Arbor, MI 48104. 3. Department of Psychiatry, University of California San Diego, 9500 Gilman Drive, MC: 0603, La Jolla, CA 92093-0603. 4. Department of Psychiatry, Center for the Study of Traumatic Stress, Uniformed Services University School of Medicine, 4301 Jones Bridge Road, Bethesda, MD 20814. 5. Division of Services and Intervention Research, National Institute of Mental Health, 6001 Executive Boulevard, Bethesda, MD 20892. 6. Department of Social and Behavioral Sciences, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115. 7. Department of Health Care Policy, Harvard Medical School, 180 Longwood Avenue, Boston, MA 02115. 8. Department of Psychology, Harvard University, 33 Kirkland Street, Cambridge, MA 02138. 9. Office of Science Policy, Planning and Communications, National Institute of Mental Health, 6001 Executive Boulevard, Bethesda, MD 20892.
Abstract
OBJECTIVES: To derive job condition scales for future studies of the effects of job conditions on soldier health and job functioning across Army Military Occupation Specialties (MOSs) and Areas of Concentration (AOCs) using Department of Labor (DoL) Occupational Information Network (O*NET) ratings. METHODS: A consolidated administrative dataset was created for the "Army Study to Assess Risk and Resilience in Servicemembers" (Army STARRS) containing all soldiers on active duty between 2004 and 2009. A crosswalk between civilian occupations and MOS/AOCs (created by DoL and the Defense Manpower Data Center) was augmented to assign scores on all 246 O*NET dimensions to each soldier in the dataset. Principal components analysis was used to summarize these dimensions. RESULTS: Three correlated components explained the majority of O*NET dimension variance: "physical demands" (20.9% of variance), "interpersonal complexity" (17.5%), and "substantive complexity" (15.0%). Although broadly consistent with civilian studies, several discrepancies were found with civilian results reflecting potentially important differences in the structure of job conditions in the Army versus the civilian labor force. CONCLUSIONS: Principal components scores for these scales provide a parsimonious characterization of key job conditions that can be used in future studies of the effects of MOS/AOC job conditions on diverse outcomes. Reprint &
OBJECTIVES: To derive job condition scales for future studies of the effects of job conditions on soldier health and job functioning across Army Military Occupation Specialties (MOSs) and Areas of Concentration (AOCs) using Department of Labor (DoL) Occupational Information Network (O*NET) ratings. METHODS: A consolidated administrative dataset was created for the "Army Study to Assess Risk and Resilience in Servicemembers" (Army STARRS) containing all soldiers on active duty between 2004 and 2009. A crosswalk between civilian occupations and MOS/AOCs (created by DoL and the Defense Manpower Data Center) was augmented to assign scores on all 246 O*NET dimensions to each soldier in the dataset. Principal components analysis was used to summarize these dimensions. RESULTS: Three correlated components explained the majority of O*NET dimension variance: "physical demands" (20.9% of variance), "interpersonal complexity" (17.5%), and "substantive complexity" (15.0%). Although broadly consistent with civilian studies, several discrepancies were found with civilian results reflecting potentially important differences in the structure of job conditions in the Army versus the civilian labor force. CONCLUSIONS: Principal components scores for these scales provide a parsimonious characterization of key job conditions that can be used in future studies of the effects of MOS/AOC job conditions on diverse outcomes. Reprint &
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