Buy this Article for $7.95

Have a coupon or promotional code? Enter it here:

When you buy this you'll get access to the ePub version, a downloadable PDF, and the ability to print the full article.


  1. Whitman, Gayle R. PhD, RN, FAAN
  2. Kim, Yookyung PhD
  3. Davidson, Lynda J. PhD, RN
  4. Wolf, Gail A. DNSc, RN, FAAN
  5. Wang, Shiaw-Ling MSN, RN


Objective: Determine the relationships between nursing staffing and specific nurse-sensitive outcomes (central line blood-associated infection, pressure ulcer, fall, medication error, and restraint application duration rates) across specialty units (cardiac and noncardiac intensive care, cardiac and noncardiac intermediate care, and medical-surgical).


Background: A number of hospital-level studies have demonstrated that lower staffing levels are associated with higher adverse patient outcomes. However, insufficient insight into unit-level staffing relationships is available. Further unit-level inquiry is necessary to fully explicate the relationships between staffing and outcomes and to provide assistance to nurse administrators as they seek to develop blueprints for staffing plans that are linked to quality outcomes.


Methods: Secondary analysis of prospective, observational data from 95 patient care units (cardiac intensive care, n = 15; noncardiac intensive care, n = 7; cardiac intermediate care, n = 18; noncardiac intermediate care, n = 12, and medical-surgical, n = 43) across 10 acute care hospitals.


Results: No statistically significant relationships were found between central line infection and pressure ulcer rates and staffing across specialty units. Significant inverse relationships were present between staffing and falls in cardiac intensive care, medication errors in both cardiac and noncardiac intensive care units, and restraint rates in the medical-surgical units.


Conclusions: Results from this study suggest that the impact of staffing on outcomes is highly variable across specialty units; however, when present, the relationships are inversely related with lower staffing levels, resulting in higher rates of all outcomes.