Impact of clinical training on recruiting graduating health professionals.

OBJECTIVES: Recruiting professional staff is an important business reason for hospitals allowing health trainees to engage in supervised patient care. Whereas prior studies have focused on educational institutions, this study focuses on teaching hospitals and whether trainees' clinical experiences affect their willingness to work (ie, recruitability) for the type of healthcare center where they trained. STUDY DESIGN: A pre-post, observational study based on Learners' Perceptions Survey data in which respondents served as their own controls. METHODS: Convenience sample of 15,207 physician, 11,844 nursing, and 13,012 associated health trainees who rotated through 1 of 169 US Department of Veterans Affairs (VA) medical centers between July 1, 2014, and June 30, 2017. Generalized estimating equations computed how clinical, learning, working, and cultural experiences influenced pre-post differences in willingness to consider VA for future employment. RESULTS: VA recruitability increased dramatically from 55% pretraining to 75% post training (adjusted odds ratio [OR], 2.1; 95% CI, 2.0-2.1; P <.001) in all 3 cohorts: physician (from 39% to 59%; OR, 1.6; 95% CI, 1.5-1.6; P <.001), nursing (from 61% to 84%; OR, 2.5; 95% CI, 2.4-2.6; P <.001), and associated health trainees (from 68% to 87%; OR, 2.7; 95% CI, 2.6-2.9; P <.001). For all trainees, changes in recruitability (P <.001) were associated with how trainees rated their clinical learning environment, personal experiences, and culture of psychological safety. Satisfaction ratings with faculty and preceptors (P <.001) were associated with positive changes in recruitability among nursing and associated health students but not physician residents, whereas nursing students who gave higher ratings for interprofessional team culture became less recruitable. CONCLUSIONS: Academic medical centers can attract their health trainees for future employment if they provide positive clinical, working, learning, and cultural experiences.

A New Method for Neural Spike Alignment: The Centroid Filter.

Recordings made directly from the nervous system are a key tool in experimental electrophysiology and the development of bioelectronic medicines. Analysis of these recordings involves the identification of signals from individual neurons, a process known as spike sorting. A critical and limiting feature of spike sorting is the need to align individual spikes in time. However, electrophysiological recordings are made in extremely noisy environments that seriously limit the performance of the spike-alignment process. We present a new centroid-based method and demonstrate its effectiveness using deterministic models of nerve signals. We show that spike alignment in the presence of noise is possible with a 30 dB reduction in minimum SNR compared with the conventional methods. We present a mathematical analysis of the centroid method, characterizing its fundamental operation and performance. Furthermore, we show that the centroid method lends itself particularly well to hardware realization, and we present results from a low-power implementation that operates on an FPGA, consuming ten times less power than conventional techniques - an important property for implanted devices. Our centroid method enables the accurate alignment of spikes in sub-0 dB SNR recordings and has the potential to enable the analysis of spikes in a wider range of environments than has been previously possible. Our method thus has the potential to influence significantly the design of electrophysiological recording systems in the future.

Improved Signal Processing Methods for Velocity Selective Neural Recording Using Multi-Electrode Cuffs.

This paper describes an improved system for obtaining velocity spectral information from electroneurogram recordings using multi-electrode cuffs (MECs). The starting point for this study is some recently published work that considers the limitations of conventional linear signal processing methods (`delay-and-add') with and without additive noise. By contrast to earlier linear methods, the present paper adopts a fundamentally non-linear velocity classification approach based on a type of artificial neural network (ANN). The new method provides a unified approach to the solution of the two main problems of the earlier delay-and-add technique, i.e., a damaging decline in both velocity selectivity and velocity resolution at high velocities. The new method can operate in real-time, is shown to be robust in the presence of noise and also to be relatively insensitive to the form of the action potential waveforms being classified.