Nurse dose: linking staffing variables to adverse patient outcomes.
ABSTRACT Inconsistent findings in more than 100 studies have made it difficult to explain how variation in nurse staffing affects patient outcomes. Nurse dose, defined as the level of nurses required to provide patient care in hospital settings, draws on variables used in staffing studies to describe the influence of many staffing variables on outcomes.
The aim of this study was to examine the construct validity of nurse dose by determining its association with methicillin-resistant Staphylococcus aureus (MRSA) infections and reported patient falls on a sample of inpatient adult acute care units.
Staffing data came from 26 units in Ontario, Canada, and Michigan. Financial and human resource data were data sources for staffing variables. Sources of data for MRSA came from infection control departments. Incident reports were the data source for patient falls. Data analysis consisted of bivariate correlations and Poisson regression.
Bivariate correlations revealed that nurse dose attributes (active ingredient and intensity) were associated significantly with both outcomes. Active ingredient (education, experience, skill mix) and intensity (full-time employees, registered nurse [RN]:patient ratio, RN hours per patient day) were significant predictors of MRSA. Coefficients for both attributes were negative and almost identical. Both attributes were significant predictors of reported patient falls, and coefficients were again negative, but coefficient sizes differed.
By conceptualizing nurse and staffing variables (education, experience, skill mix, full-time employees, RN:patient ratio, RN hours per patient day) as attributes of nurse dose and by including these in the same analysis, it is possible to determine their relative influence on MRSA infections and reported patient falls.
Article: Understanding Poisson Regression.[Show abstract] [Hide abstract]
ABSTRACT: Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes. [J Nurs Educ. 2014;53(x):xxx-xxx.].The Journal of nursing education. 03/2014; 53(4):1-9.
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ABSTRACT: Maintaining cost-effective care while optimizing patient outcomes becomes more challenging because the complexity of health care increases. Numerous variables impact patient outcomes. The purpose of this article is to describe recent empirical literature regarding nurse-related variables that impact patient outcomes. Multiple variables are described, including the work environment, Magnet status, nurse–physician communication, job demands, staffing, level of education, years of nursing experience, and certification. Staffing remains the most consistent positive influence on patient outcomes.Teaching and Learning in Nursing 10/2013; 8(4):120–127.
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ABSTRACT: In hospitals, nurses provide patient care around the clock, but the impact of night staff characteristics on patient outcomes is not well understood. The aim of this study was to examine the association between night nurse staffing and workforce characteristics and the length of stay (LOS) in 138 veterans affairs (VA) hospitals using panel data from 2002 through 2006. Staffing in hours per patient day was higher during the day than at night. The day nurse workforce had more educational preparation than the night workforce. Nurses' years of experience at the unit, facility, and VA level were greater at night. In multivariable analyses controlling for confounding variables, higher night staffing and a higher skill mix were associated with reduced LOS. © 2014 Wiley Periodicals, Inc.Research in Nursing & Health 01/2014; · 1.16 Impact Factor