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Predictors of job strain in residential dementia care nursing staff.

AbstractAIM:
To identify predictors of job strain in residential nursing care staff working with people with dementia.
BACKGROUND:
It is well known that nursing staff experience high levels of stress, but less is known about how to predict job strain.
METHODS:
The job strain of nursing care staff (n = 344) within residential dementia care settings was assessed. Standard linear regression analysis was used to explore predictors of job strain.
RESULT:
Data from the study shows that nursing staff in residential dementia care have a demanding job and experience high levels of strain. The linear regression model with four predictor variables explained 19% of the variability in job strain scores. Perceived caring climate of the unit, staff education level, possibilities to have discussions of difficulties and ethics at work and staff age, had a statistically significant association with job strain.
CONCLUSIONS:
The caring climate, staff education, reflective practice and staff age can be used as screening variables when predicting job strain.
IMPLICATIONS FOR NURSING MANAGEMENT:
These predictors can assist managers and directors to identify targeted strategies for supervision and support of nursing staff to secure their well-being, and by that securing the quality of care provided to residents.
AuthorsDavid Edvardsson, Per-Olof Sandman, Rhonda Nay, Stig Karlsson
JournalJournal of nursing management (J Nurs Manag) Vol. 17 Issue 1 Pg. 59-65 (Jan 2009) ISSN: 1365-2834 [Electronic] England
PMID19166523 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
Topics
  • Adaptation, Psychological
  • Adult
  • Aged, 80 and over
  • Dementia (nursing)
  • Female
  • Humans
  • Job Satisfaction
  • Linear Models
  • Male
  • Models, Statistical
  • Multivariate Analysis
  • Nursing Staff
  • Occupational Health (statistics & numerical data)
  • Residential Facilities (statistics & numerical data)
  • Stress, Psychological
  • Surveys and Questionnaires

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