Paterson B.,Thompson Rivers University |
Hirsch G.,Capital |
Andres K.,Thompson Rivers University
International Journal of Drug Policy | Year: 2013
Background: Interventions to mediate the stigmatization of people affected with HCV, particularly those who use illicit drugs, have been largely focused on changing health care practitioners' attitudes and knowledge regarding Hepatitis C and illicit drug use and these have had disappointing results. There is a need for research that examines factors beyond individual practitioners that explains why and how stigmatization of the population occurs within health care and informs interventions to mitigate these factors. Methods: The research was intended to identify structural factors that contribute to the structural stigmatization of people within hospital Emergency Departments who are current users of illicit drugs and are HCV positive. The research had an interpretive description design and occurred in Nova Scotia, Canada. The year-long qualitative study entailed individual interviews of 50 service providers in hospital EDs or community organizations that served this population. Results: The research findings generated a model of structural stigmatization that greatly expands the current understanding of stigmatization beyond individual practitioners' attitudes and knowledge and internal structures to incorporate structures external to hospitals, such as physician shortages within the community and the mandate of EDs to reduce wait times. Conclusions: The research reported herein has conceptualized stigmatization beyond an individualistic approach to incorporate the multifaceted ways that such stigmatization is fostered and supported by internal and external structures. © 2013 Elsevier B.V.
Rossomando F.G.,Capital |
Neural Computing and Applications | Year: 2016
Abstract: This work presents a discrete-time sliding mode neuro-adaptive control (DTSMNAC) method for robot manipulators. Due to the dynamics variations and uncertainties in the robot model, the trajectory tracking of robot manipulators has been one of the research areas for the last years. The proposed control structure is a practical design that combines a discrete-time neuro-adaptation technique with sliding mode control to compensate the dynamics variations in the robot. Using an online adaptation technique, a DTSMNAC controller is used to approximate the equivalent control in the neighborhood of the sliding surface. A sliding control is included to guarantee that the discrete-time neural sliding mode control can improve a stable closed-loop system for the trajectory tracking control of the robot with dynamics variations. The proposed technique simultaneously ensures the stability of the adaptation of the neural networks and can be obtained a suitable equivalent control when the parameters of the robot dynamics are unknown in advance. This neural adaptive system is applied to a SCARA robot manipulator and shows to be able to ensure that the output tracking error will converge to zero. Finally, experiments on a SCARA robot have been developed to show the performance of the proposed technique, including the comparison with a PID controller. © 2016 The Natural Computing Applications Forum