TITLE:
Adaptive Classification Methods for Predicting Transitions in the Nursing Workforce
AUTHORS:
George J. Knafl, Mark Toles, Anna S. Beeber, Cheryl B. Jones
KEYWORDS:
Adaptive Classification, LPN-to-RN Transition, LPN Workforce
JOURNAL NAME:
Open Journal of Statistics,
Vol.8 No.3,
June
13,
2018
ABSTRACT: Earlier analyses of transitions from licensed
practical nurse (LPN) to registered nurse (RN) in the North Carolina (NC)
nursing workforce in terms of 11 categorical predictors were limited by not
considering parsimonious classifications based on these predictors and by
substantial amounts of missing data. To address these issues, we formulated
adaptive classification methods. Secondary analyses of data collected by the NC
State Board of Nursing were also conducted to demonstrate adaptive
classification methods by modeling the occurrence of LPN-to-RN transitions in
the NC nursing workforce from 2001-2013. These methods combine levels (values)
for one or more categorical predictors into parsimonious classifications.
Missing values for a predictor are treated as one level for that predictor so
that the complete data can be used in the analyses; the missing level is
imputed by combining it with other levels of a predictor. An adaptive nested
classification generated the best model for predicting an LPN-to-RN transition
based on three predictors in order of importance: year of first LPN licensure,
work setting at transition, and age at first LPN licensure. These results
demonstrate that adaptive classification can identify effective and
parsimonious classifications for predicting dichotomous outcomes such as the
occurrence of an LPN-to-RN transition.