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.
Highlights
These results demonstrate that adaptive classification can identify effective and parsimonious classifications for predicting dichotomous outcomes such as the occurrence of an licensed practical nurse (LPN)-to-registered nurse (RN) transition
In a previous analysis of nursing workforce data modeling the occurrence of a transition from a licensed practical nurse (LPN) to a registered nurse (RN) [1], it was anticipated that study findings would inform ongoing efforts to understand the supply and behaviors of the nursing workforce
The threshold for a substantial percent decrease in the Likelihood cross-validation (LCV) and Akaike information criterion (AIC)+ scores for 37,781 observations, DF = 1, and significance level α = 0.001 was PD(D, n) = 0.014% with D = δ(0.999, 1) = 10.82757
Summary
In a previous analysis of nursing workforce data modeling the occurrence of a transition from a licensed practical nurse (LPN) to a registered nurse (RN) [1], it was anticipated that study findings would inform ongoing efforts to understand the supply and behaviors of the nursing workforce. To achieve these aims, logistic regression analyses were conducted using 11 categorical characteristics as predictors, first generating unadjusted models one predictor at a time, and generating a composite model using all 11 predictors in combination. No attempt was made to remove extraneous terms from models, so generated models included non-significant terms (with p-values as large as 0.974 in unadjusted models and 0.958 in the composite model) To address these analysis issues, an exploratory approach was needed to systematically generate a parsimonious model using available categorical predictors while allowing for missing data and accounting for the large sample size. Reported analyses adjusted for the large sample size of the NC LPN workforce data by conservatively setting the significance level α to 0.001 rather than to the conventional value 0.05. LCV scores can be approximated for large enough sample sizes [4] by Akaike information criterion (AIC) scores [5], which can be used in such cases to reduce the computation time
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