Journal article
Predictive Model to Determine Need for Nursing Workforce
Policy, politics & nursing practice, Vol.5(3), pp.174-190
08/2004
DOI: 10.1177/1527154404266785
Abstract
This article describes a statistical modeling study designed to improve targets of need for registered nurse (RN) workforce. The model is place-based and incorporates the concepts of clinical need and regional service utilization. A cross-sectional study was conducted in Nebraska (1993-1999), and the unit of study was the county (N = 66). A mixed-model approach was used, and five predictor variables (% age 20-44,% age 45-64,% age 65+,% White non-Hispanic, and area) were significantly (p < .001) associated with service demand. Coefficient estimates were applied to various population projection scenarios, and the model’s algorithm converted service demand into number of RNs needed to compare numbers of RNs employed with projected need. The implications for RN workforce policy and funding decisions—at both federal and state levels—are significant. Further research with a larger, multistate database will be conducted to refine the model and demonstrate generalizability.
Details
- Title: Subtitle
- Predictive Model to Determine Need for Nursing Workforce
- Creators
- Mary E Cramer - University of Nebraska College of Nursing and College of Medicine, Department of Preventive and Societal MedicineLi-Wu ChenKeith J Mueller - Nebraska Center for Rural Health Research, University of Nebraska Medical CenterMichael Shambaugh-Miller - Department of Preventive and Societal Medicine at the University of Nebraska Medical Center (UNMC)Sangeeta Agrawal - University of Nebraska Medical Center (UNMC), College of Nursing
- Resource Type
- Journal article
- Publication Details
- Policy, politics & nursing practice, Vol.5(3), pp.174-190
- DOI
- 10.1177/1527154404266785
- ISSN
- 1527-1544
- eISSN
- 1552-7468
- Language
- English
- Date published
- 08/2004
- Academic Unit
- Health Management and Policy; Public Policy Center (Archive)
- Record Identifier
- 9984214834702771
Metrics
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