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J Adv Nurs. 2003 Oct;44(2):217-26.

Nursing staff mix models and outcomes.

Author information

1
Canadian Institutes of Health Research. l.mcgillishall@utoronto.ca

Abstract

BACKGROUND:

Recently, restructuring of the nursing workforce has been undertaken in a number of countries in an effort to provide efficient and cost-effective services to users. This often takes the form of the introduction of unregulated workers to carry out support roles with registered nurses. However, these changes have not been evaluated for efficacy or impact on nurses, patients or the health care system.

PURPOSE:

The purpose of this study was to determine the relationship between staff mix models comprising regulated staff (Registered Nurses and Registered Practical Nurses) or regulated and unregulated staff (Registered Nurses and unregulated workers), and nursing and quality outcomes.

METHODS:

This comparative correlational study was conducted in a random sample of 30 adult, acute care patient units within eight hospitals located in Toronto, Canada. Registered Nurses employed on 30 randomly selected hospital units, grouped by the two staff mix models (15 units per group), were surveyed using previously validated instruments to measure role conflict, role ambiguity, job satisfaction, perceived effectiveness of care and perceived quality of care.

RESULTS:

Results indicated that Registered Nurses in this study experienced high levels of role conflict, regardless of the type of staff mix model within which they worked. Registered Nurses on units employing both Registered Nurses and unregulated workers reported higher levels of job satisfaction. On units employing both Registered Nurses and unregulated workers, Registered Nurses perceived that the quality of care was lower.

CONCLUSIONS:

Staff mix model was related to Registered Nurses' perceptions of the quality of patient care. It was also evident that other variables within the work environment might have more influence on the outcomes examined than the independent variable of staff mix.

PMID:
14521688
[Indexed for MEDLINE]

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