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1 – 10 of 121Soo-Hoon Lee, Thomas W. Lee and Phillip H. Phan
Workplace voice is well-established and encompasses behaviors such as prosocial voice, informal complaints, grievance filing, and whistleblowing, and it focuses on interactions…
Abstract
Workplace voice is well-established and encompasses behaviors such as prosocial voice, informal complaints, grievance filing, and whistleblowing, and it focuses on interactions between the employee and supervisor or the employee and the organizational collective. In contrast, our chapter focuses on employee prosocial advocacy voice (PAV), which the authors define as prosocial voice behaviors aimed at preventing harm or promoting constructive changes by advocating on behalf of others. In the context of a healthcare organization, low quality and unsafe patient care are salient and objectionable states in which voice can motivate actions on behalf of the patient to improve information exchanges, governance, and outreach activities for safer outcomes. The authors draw from the theory and research on responsibility to intersect with theories on information processing, accountability, and stakeholders that operate through voice between the employee-patient, employee-coworker, and employee-profession, respectively, to propose a model of PAV in patient-centered healthcare. The authors complete the model by suggesting intervening influences and barriers to PAV that may affect patient-centered outcomes.
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Joshua C. C. Chan, Chenghan Hou and Thomas Tao Yang
Importance sampling is a popular Monte Carlo method used in a variety of areas in econometrics. When the variance of the importance sampling estimator is infinite, the central…
Abstract
Importance sampling is a popular Monte Carlo method used in a variety of areas in econometrics. When the variance of the importance sampling estimator is infinite, the central limit theorem does not apply and estimates tend to be erratic even when the simulation size is large. The authors consider asymptotic trimming in such a setting. Specifically, the authors propose a bias-corrected tail-trimmed estimator such that it is consistent and has finite variance. The authors show that the proposed estimator is asymptotically normal, and has good finite-sample properties in a Monte Carlo study.
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