Predicting of pain, disability, and sick leave regarding a non-clinical sample among Swedish nurses
2010 (English)In: Scandinavian Journal of Pain, ISSN 1877-8860, E-ISSN 1877-8879, Vol. 1, no 3, p. 160-166Article in journal (Refereed) Published
Abstract [en]
Objectives: Health care providers, especially registered nurses (RNs), are a professional group with a high risk of musculoskeletal pain (MSP). This longitudinal study contributes to the literature by describing the prevalence and change in MSP, work-related factors, personal factors, self reported pain, disability and sick leave (> 7 days) among RNs working in a Swedish hospital over a three-year period. Further, results concerning prediction of pain, disability and sick leave from baseline to a three-year follow-up are reported. Method: In 2003, a convenience sample of 278 RNs (97.5% women, mean age 43 years) completed a questionnaire. In 2006, 244 RNs (88% of the original sample) were located, and 200 (82%) of these completed a second questionnaire. Results: Logistic regression analyses revealed that pain, disability and sick leave at baseline best predicted pain, disability, and sick leave at follow-up. The personal factors self rated health and sleep quality during the last week predicted pain at follow-up, while age, self rated health, and considering yourself as optimist or pessimist predicted disability at follow-up, however weakly. None one of the work- related factors contributed significantly to the regression solution. Conclusions: The results support earlier studies showing that a history of pain and disability is predictive of future pain and disability. Attention to individual factors such as personal values may be needed in further research.
Place, publisher, year, edition, pages
2010. Vol. 1, no 3, p. 160-166
Keywords [en]
registered nurses, musculoskeletal pain, work-related factors, personal factors, sickness absence, disability
National Category
Nursing
Identifiers
URN: urn:nbn:se:hig:diva-7588DOI: 10.1016/j.sjpain.2010.05.029PubMedID: 29913982Scopus ID: 2-s2.0-77955045230OAI: oai:DiVA.org:hig-7588DiVA, id: diva2:351357
2010-09-212010-09-142020-11-23Bibliographically approved