Employee 'words' on high involvement work practices, value congruence and burnout

This article examined the impact of perceived high involvement work practices (HIWPs) on person-organization value congruence (P-O fit) and long term burnout. The study was conducted in a Canadian general hospital. Findings from structural equation modeling (N = 185) revealed that perceived HIWPs were significantly positively associated with P-O fit. While there was no direct effect of HIWPs on burnout, P-O fit fully mediated the relationship between perceptions of HIWPs and burnout. We discuss the implications of these findings for our understanding of HIWPs influence on P-O fit and burnout.

Perceptions of high involvement work practices and burnout: investigating the mediating role of procedural justice and role overload and the moderating role of colleague support

Purpose
The purpose of this paper is to investigate the impact of employees’ perceptions of high involvement work practices (HIWPs) on burnout (emotional exhaustion and depersonalisation) via the mediating role of role overload and procedural justice. Further, perceived colleague support was hypothesised to moderate the effects of role overload and procedural justice on these outcomes.

Design/Methodology
The study was conducted on a random sample of unionised registered nurses (RNs) working in the Canadian public health care sector, stratified by mission and size of the institution to ensure representativeness. Of the 6546 nurses solicited, 2174 returned a completed questionnaire, resulting in a response rate of 33.2%. To test our hypotheses we conducted structural equation modelling (SEM) in Mplus version 6.0 (Muthen and Muthen, 1998 – 2010) with Maximum Likelihood (ML) estimation.

Results
The results showed that procedural justice and role overload fully mediated the influence of HIWPs on burnout. Moreover, colleague support moderated the effects of procedural justice and role overload on emotional exhaustion but not depersonalisation.

Limitations
The study used a cross-sectional research design and is conducted among one occupational group (i.e. nurses).

Research/Practical Implications
The findings question the dark side of HRM in the health care context. They also contribute to the lack of theoretical and empirical work dedicated to understanding the ‘black box’ problem (Castanheira and Chambel, 2010).

Originality/Value
The study employs a well-known theoretical perspective from the occupational health psychology literature to the HR field in order to contribute to the lack of theorising in the HR-well-being link.

Delivering a Research-Enabled Multistakeholder Partnership for Enhanced Patient Care at a Population Level: The Northern Ireland Comprehensive Cancer Program

The last 20 years have seen significant advances in cancer care in Northern Ireland, leading to measureable improvements in patient outcomes. Crucial to this transformation has been an ethos that recognizes the primacy role of research in effecting heath care change. The authors' model of a cross-sectoral partnership that unites patients, scientists, health care professionals, hospital trusts, bioindustry, and government agencies can be truly transformative, empowering tripartite clinical-academic-industry efforts that have already yielded significant benefit and will continue to inform strategy and its implementation going forward.

Hidden Markov Models With Set-Valued Parameters

Hidden Markov models (HMMs) are widely used probabilistic models of sequential data. As with other probabilistic models, they require the specification of local conditional probability distributions, whose assessment can be too difficult and error-prone, especially when data are scarce or costly to acquire. The imprecise HMM (iHMM) generalizes HMMs by allowing the quantification to be done by sets of, instead of single, probability distributions. iHMMs have the ability to suspend judgment when there is not enough statistical evidence, and can serve as a sensitivity analysis tool for standard non-stationary HMMs. In this paper, we consider iHMMs under the strong independence interpretation, for which we develop efficient inference algorithms to address standard HMM usage such as the computation of likelihoods and most probable explanations, as well as performing filtering and predictive inference. Experiments with real data show that iHMMs produce more reliable inferences without compromising the computational efficiency.

The Complexity of MAP Inference in Bayesian Networks Specified Through Logical Languages

We study the computational complexity of finding maximum a posteriori configurations in Bayesian networks whose probabilities are specified by logical formulas. This approach leads to a fine grained study in which local information such as context-sensitive independence and determinism can be considered. It also allows us to characterize more precisely the jump from tractability to NP-hardness and beyond, and to consider the complexity introduced by evidence alone.