Employment among Working-Age Adults with Multiple Sclerosis: A Data-Mining Approach to Identifying Employment Interventions

Autor/inn/en	Bishop, Malachy; Chan, Fong; Rumrill, Phillip D., Jr.; Frain, Michael P.; Tansey, Timothy N.; Chiu, Chung-Yi; Strauser, David; Umeasiegbu, Veronica I.
Titel	Employment among Working-Age Adults with Multiple Sclerosis: A Data-Mining Approach to Identifying Employment Interventions
Quelle	In: Rehabilitation Research, Policy, and Education, 29 (2015) 2, S.135-152 (18 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2168-6653
DOI	10.1891/2168-6653.29.2.135
Schlagwörter	Employment Level; Diseases; Chronic Illness; Neurological Impairments; Adults; Data Collection; Intervention; Accessibility (for Disabled); Predictor Variables; National Surveys; Individual Characteristics; Symptoms (Individual Disorders); Interaction; Social Services; Comparative Analysis; Physical Disabilities; Data Analysis; Information Retrieval + Suchen Sie Ihr Suchwort? Beschäftigungsgrad; Disease; Krankheit; Chronic disease; Chronische Krankheit; Neurodegenerative Erkrankung; Data capture; Datensammlung; Accessibility; Zugänglichkeit; Prädiktor; Personality characteristic; Personality traits; Persönlichkeitsmerkmal; Psychiatrische Symptomatik; Interaktion; Social service; Soziale Dienstleistung; Soziale Dienste; Physical handicap; Körperbehinderung; Auswertung
Abstract	Purpose: To examine demographic, functional, and clinical multiple sclerosis (MS) variables affecting employment status in a national sample of adults with MS in the United States. Method: The sample included 4,142 working-age (20-65 years) Americans with MS (79.1% female) who participated in a national survey. The mean age of participants was 51.93 years (SD = 8.7). The dependent variable was employment status. The predictor variables included a set of demographic, functional, and MS variables. Results: The chi-squared automatic interaction detector (CHAID) analysis indicated that participants who were receiving Social Security Disability Insurance (SSDI) had significantly lower rates of employment (8.6%) than those who were not receiving SSDI (53.9%). For those not receiving SSDI, the most important factor predicting employment status was MS impact on physical functioning, as measured with the Multiple Sclerosis Impact Scale Physical Impact scale. Conclusion: The data-mining approach (i.e., CHAID analysis) provided detailed information and insight about interactions among demographic, functional, and clinical variables and employment status through the segmentation of the sample into mutually exclusive homogeneous subgroups. Implications for rehabilitation intervention, based on these subgroupings, are discussed. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01