title: Understanding unassisted falls: effects of nurse staffing level and nursing staff characteristics.
abstract: Hierarchical Poisson modeling was used to explore hospital and nursing unit characteristics as predictors of the unassisted fall rate. Longitudinal data were collected from 1502 units in 248 US hospitals. The relation between the fall rate and total nurse staffing was positive at lower staffing levels and negative for levels around and above the median. The fall rate was negatively associated with registered nurse skill mix and average registered nurse tenure on the unit.
authors.affiliation: Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS 66160, USA. vstaggs@kumc.edu
authors.forename: Vincent S
authors.initials: VS
authors.lastname: Staggs
authors.forename: Jeff E
authors.initials: JE
authors.lastname: Knight
authors.forename: Nancy
authors.initials: N
authors.lastname: Dunton
mesh_terms: D000058:Accidental Falls / Q000706:statistics & numerical data*; D006757:Hospital Units / Q000706:statistics & numerical data; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D009732:Nursing Care / Q000592:standards; D015403:Nursing Evaluation Research; D009741:Nursing Staff, Hospital / Q000706:statistics & numerical data* / Q000600:supply & distribution*; D061214:Patient Safety; D016012:Poisson Distribution; D011787:Quality of Health Care; D014481:United States; D000078329:Workforce