Management decision-making in nursing

This study developed a new, valid and reliable evaluation instrument to measure the level, type and pattern of management decisions of fifteen nursing students. The management decision score achieved using this instrument was correlated with two psychological determinants of management decision making: creativity and problem-solving ability. The instrument was a written patient management problem in case format, answered by a free form written response. The student responses were classified for type of management decision according to the sub-categories of technical, inter-personal, environmental and unique. Using statistical analysis a significant difference was found in the type of management decisions most frequently selected by the study sample. The students predominantly selected technical type decisions. This preference for one type of management decision may be due to a number of psychological and environmental factors. These factors may program and mold the type of management decisions student nurses make early in their career. Low but positive correlations were found between the total management score and the two psychological tests. This finding supports the authors cited in the literature who state that although creativity augments the type of management decision making, it is not present or encouraged widely in the nursing profession. These factors are worth considering when the profession becomes concerned over ritualization and lack of individuality in patient care. The tool is easy to administer, lends itself to a variety of professional settings and shows promise with further refinement for computer application.

Optimization of trial wave functions for use in quantum Monte Carlo with application to LiH

Methods for both partial and full optimization of wavefunction parameters are explored, and these are applied to the LiH molecule. A partial optimization can be easily performed with little difficulty. But to perform a full optimization we must avoid a wrong minimum, and deal with linear-dependency, time step-dependency and ensemble-dependency problems. Five basis sets are examined. The optimized wavefunction with a 3-function set gives a variational energy of -7.998 + 0.005 a.u., which is comparable to that (-7.990 + 0.003) 1 of Reynold's unoptimized \fin ( a double-~ set of eight functions). The optimized wavefunction with a double~ plus 3dz2 set gives ari energy of -8.052 + 0.003 a.u., which is comparable with the fixed-node energy (-8.059 + 0.004)1 of the \fin. The optimized double-~ function itself gives an energy of -8.049 + 0.002 a.u. Each number above was obtained on a Bourrghs 7900 mainframe computer with 14 -15 hrs CPU time.

Application of Reptation Quantum Monte Carlo and Related Methods to LiH

This work investigates mathematical details and computational aspects of Metropolis-Hastings reptation quantum Monte Carlo and its variants, in addition to the Bounce method and its variants. The issues that concern us include the sensitivity of these algorithms' target densities to the position of the trial electron density along the reptile, time-reversal symmetry of the propagators, and the length of the reptile. We calculate the ground-state energy and one-electron properties of LiH at its equilibrium geometry for all these algorithms. The importance sampling is performed with a single-determinant large Slater-type orbitals (STO) basis set. The computer codes were written to exploit the efficiencies engineered into modern, high-performance computing software. Using the Bounce method in the calculation of non-energy-related properties, those represented by operators that do not commute with the Hamiltonian, is a novel work. We found that the unmodified Bounce gives good ground state energy and very good one-electron properties. We attribute this to its favourable time-reversal symmetry in its target density's Green's functions. Breaking this symmetry gives poorer results. Use of a short reptile in the Bounce method does not alter the quality of the results. This suggests that in future applications one can use a shorter reptile to cut down the computational time dramatically.