Acadia University

About: Acadia University is a education organization based out in Wolfville, Nova Scotia, Canada. It is known for research contribution in the topics: Population & Wireless sensor network. The organization has 1903 authors who have published 3881 publications receiving 90517 citations. The organization is also known as: Queen's College.

Early predictors of job burnout and engagement.

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.

Work engagement: An emerging concept in occupational health psychology

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.

The impact of interpersonal environment on burnout and organizational commitment

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.

BART: Bayesian additive regression trees

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.