MODELLING THE NURSE MANPOWER SYSTEM.
Contributors: University Of Ulster (Northern Ireland)
Published: 1989
Number of pages: 240
Subjects: Health Sciences, Nursing.
Description: Available from UMI in association with The British Library.

The overall aim of this work is to develop methods which will allow reliable estimates to be calculated of the various staff flows associated with the NI Nursing Service. These estimates will be calculated using data from the Nurse Personnel File, a typical manpower data set which is statistically incomplete due to both left truncation and right censoring.

We derive a maximum likelihood estimate of the non-parametric survivor function, which extends Kaplan and Meier's estimate to include left truncated data. Maximum likelihood estimates for three parametric distributions are also obtained. These cls models are fitted to each of the employment grades of the NI Nursing Service, and the ability of the models to predict the number of future leavers is tested using a specially constructed chi-squared test.

The estimation techniques developed for cls are modified to allow maximum likelihood estimates for non-parametric, parametric and network models of duration of stay in 'limbo', and the probability of eventual return to service to be calculated. The ability of these models to predict the number of future returners is also tested.

Maximum likelihood estimates for non-parametric cls and 'limbo' competing risks models are derived together with asymptotic estimates of variances. These models are fitted to each grade of the NI Nursing Service and their ability to predict future events is tested using a multivariate form of the chi-squared test derived earlier. We show that the estimation techniques used to fit the non-parametric competing risks model can be adapted to allow a non-parametric semi-Markov model to be fitted to the complete NI Nursing System. We also describe a method of representing the non-parametric semi-Markov model as a Markov chain, and show how this simplified model of the complete NI Nursing System can be used to predict future staff stocks from current staff stocks.
Notes: Source: Dissertation Abstracts International, Volume: 51-03, Section: B, page: 1192.

Thesis (PH.D.)--UNIVERSITY OF ULSTER (NORTHERN IRELAND), 1989.

School code: 5075.