Getting to know a stranger-rural nurses' experiences of mentoring : a grounded theory

Background: Mentoring is often proposed as a solution to the problem of successfully recruiting and retaining nursing staff. The aim of this constructivist grounded theory study was to explore Australian rural nurses' experiences of mentoring. Design: The research design used was reflexive in nature resulting in a substantive, constructivist grounded theory study. Participants: A national advertising campaign and snowball sampling were used to recruit nine participants from across Australia. Participants were rural nurses who had experience in mentoring others. Methods: Standard grounded theory methods of theoretical sampling, concurrent data collection and analysis using open, axial and theoretical coding and a story line technique to develop the core category and category saturation were used. To cultivate the reflexivity required of a constructivist study, we also incorporated reflective memoing, situational analysis mapping techniques and frame analysis. Data was generated through eleven interviews, email dialogue and shared situational mapping. Results: Cultivating and growing new or novice rural nurses using supportive relationships such as mentoring was found to be an existing, integral part of experienced rural nurses' practice, motivated by living and working in the same communities. Getting to know a stranger is the first part of the process of cultivating and growing another. New or novice rural nurses gain the attention of experienced rural nurses through showing potential or experiencing a critical incidence. Conclusions: The problem of retaining nurses is a global issue. Experienced nurses engaged in clinical practice have the potential to cultivate and grow new or novice nurses-many already do so. Recognising this role and providing opportunities for development will help grow a positive, supportive work environment that nurtures the experienced nurses of tomorrow.

Accelerated leap methods for simulating discrete stochastic chemical kinetics

Biologists are increasingly conscious of the critical role that noise plays in cellular functions such as genetic regulation, often in connection with fluctuations in small numbers of key regulatory molecules. This has inspired the development of models that capture this fundamentally discrete and stochastic nature of cellular biology - most notably the Gillespie stochastic simulation algorithm (SSA). The SSA simulates a temporally homogeneous, discrete-state, continuous-time Markov process, and of course the corresponding probabilities and numbers of each molecular species must all remain positive. While accurately serving this purpose, the SSA can be computationally inefficient due to very small time stepping so faster approximations such as the Poisson and Binomial τ-leap methods have been suggested. This work places these leap methods in the context of numerical methods for the solution of stochastic differential equations (SDEs) driven by Poisson noise. This allows analogues of Euler-Maruyuma, Milstein and even higher order methods to be developed through the Itô-Taylor expansions as well as similar derivative-free Runge-Kutta approaches. Numerical results demonstrate that these novel methods compare favourably with existing techniques for simulating biochemical reactions by more accurately capturing crucial properties such as the mean and variance than existing methods.