Education•Wolfville, Nova Scotia, Canada•
About: Acadia University is a(n) education organization based out in Wolfville, Nova Scotia, Canada. It is known for research contribution in the topic(s): Population & Wireless sensor network. The organization has 1903 authors who have published 3881 publication(s) receiving 90517 citation(s). The organization is also known as: Queen's College.
Topics: Population, Wireless sensor network, Key distribution in wireless sensor networks, Burnout, Terrane
Papers published on a yearly basis
TL;DR: A longitudinal study predicted changes in burnout or engagement a year later by identifying 2 types of early indicators at the initial assessment, and discussed in terms of the enhanced ability to customize interventions for targeted groups within the workplace.
Abstract: A longitudinal study predicted changes in burnout or engagement a year later by identifying 2 types of early indicators at the initial assessment. Organizational employees (N = 466) completed measures of burnout and 6 areas of worklife at 2 times with a 1-year interval. Those people who showed an inconsistent pattern at Time 1 were more likely to change over the year than were those who did not. Among this group, those who also displayed a workplace incongruity in the area of fairness moved to burnout at Time 2, while those without this incongruity moved toward engagement. The implications of these 2 predictive indicators are discussed in terms of the enhanced ability to customize interventions for targeted groups within the workplace.
17 Sep 2008-Work & Stress
TL;DR: The emerging concept of work engagement is introduced: a positive, fulfilling, affective-motivational state of work-related well-being that is characterized by vigour, dedication, and absorption.
Abstract: This position paper introduces the emerging concept of work engagement: a positive, fulfilling, affective-motivational state of work-related well-being that is characterized by vigour, dedication, and absorption. Although there are different views of work engagement, most scholars agree that engaged employees have high levels of energy and identify strongly with their work. The most often used instrument to measure engagement is the Utrecht Work Engagement Scale, a self-report instrument that has been validated in many countries across the world. Research on engagement has investigated how engagement differs from related concepts (e.g., workaholism, organizational commitment), and has focused on the most important predictors of work engagement. These studies have revealed that engagement is a unique concept that is best predicted by job resources (e.g., autonomy, supervisory coaching, performance feedback) and personal resources (e.g., optimism, self-efficacy, self-esteem). Moreover, the first studies have shown that work engagement is predictive of job performance and client satisfaction. The paper closes with an account of what we do not know about work engagement, and offers a brief research agenda for future work.
TL;DR: In this paper, the authors found that high burnout was related to diminished organizational commitment, which was also related to aspects of the interpersonal environment of the organization, and that frequent contact with personnel in the organization is related to the development of burnout at each stage.
Abstract: Summary Organizational commitment and burnout were related to interpersonal relationships of nurses in a small general hospital. Regular communication contacts among personnel were differentiated as supervisor or coworker contact, and these categories were further differentiated into pleasant and unpleasant contacts. The results were consistent with a view of burnout in which emotional exhaustion leads to greater depersonalization which subsequently leads to diminished personal accomplishment. Interpersonal contact with personnel in the organization was related to the development of burnout at each stage. Patterns of pleasant and unpleasant contacts with supervisors and coworkers were related to the three aspects of burnout in a distinct manner. High burnout was related to diminished organizational commitment, which was also related to aspects of the interpersonal environment of the organization. The results are discussed in the context of a comprehensive approach to psychological adjustment to a worksetting.
TL;DR: The roots of the burnout concept seem to be embedded within broad social, economic, and cultural developments that took place in the last quarter of the past century and signify the rapid and profound transformation from an industrial society into a service economy as mentioned in this paper.
Abstract: Purpose – The purpose of this paper is to focus on the career of the burnout concept itself, rather than reviewing research findings on burnout.Design/methodology/approach – The paper presents an overview of the concept of burnout.Findings – The roots of the burnout concept seem to be embedded within broad social, economic, and cultural developments that took place in the last quarter of the past century and signify the rapid and profound transformation from an industrial society into a service economy. This social transformation goes along with psychological pressures that may translate into burnout. After the turn of the century, burnout is increasingly considered as an erosion of a positive psychological state. Although burnout seems to be a global phenomenon, the meaning of the concept differs between countries. For instance, in some countries burnout is used as a medical diagnosis, whereas in other countries it is a non‐medical, socially accepted label that carries a minimum stigma in terms of a psyc...
TL;DR: A Bayesian "sum-of-trees" model where each tree is constrained by a regularization prior to be a weak learner, and fitting and inference are accomplished via an iterative Bayesian backfitting MCMC algorithm that generates samples from a posterior.
Abstract: We develop a Bayesian “sum-of-trees” model where each tree is constrained by a regularization prior to be a weak learner, and fitting and inference are accomplished via an iterative Bayesian backfitting MCMC algorithm that generates samples from a posterior. Effectively, BART is a nonparametric Bayesian regression approach which uses dimensionally adaptive random basis elements. Motivated by ensemble methods in general, and boosting algorithms in particular, BART is defined by a statistical model: a prior and a likelihood. This approach enables full posterior inference including point and interval estimates of the unknown regression function as well as the marginal effects of potential predictors. By keeping track of predictor inclusion frequencies, BART can also be used for model-free variable selection. BART’s many features are illustrated with a bake-off against competing methods on 42 different data sets, with a simulation experiment and on a drug discovery classification problem.
Showing all 1903 results
|Anthony P. Farrell||92||495||29992|
|Paul B. Corkum||88||576||37200|
|Michael P. Leiter||67||168||28528|
|Gerard van Koten||66||583||20488|
|George K. Iwama||56||122||12672|
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