The work-based predictors of job engagement and job satisfaction experienced by community health professionals

Background: Job engagement represents a critical resource for community-based health care agencies to achieve high levels of effectiveness. However, studies examining the organisational sources of job engagement among health care professionals have generally overlooked those workers based in community settings.
Purpose: This study drew on the demand-control model, in addition to stressors that are more specific to community health services (e.g., unrewarding management practices), to identify conditions that are closely associated with the engagement experienced by a community health workforce. Job satisfaction was also included as a way of assessing how the predictors of job engagement differ from those associated with other job attitudes.
Methodology/Approach: Health and allied health care professionals (n = 516) from two
Australian community health services took part in the current investigation. Responses from the two organisations were pooled and analysed using linear multiple regression.
Findings: The analyses revealed that three working conditions were predictive of both job engagement and job satisfaction (i.e., job control, quantitative demands and unrewarding management practices). There was some evidence of differential effects with cognitive demands being associated with job engagement, but not job satisfaction.
Practice Implications: The results provide important insights into the working conditions that, if addressed, could play key roles in building a more engaged and satisfied community health workforce. Further, working conditions like job control and management practices are amenable to change and thus represent important areas where community health services could enhance the energetic and motivational resources of their employees.

An empirical study to review and revise job responsibilities of software testers

The broad domain of software testing includes different job responsibilities such as creating test plans, devising and running a variety of tests, documenting results, to liaising between different development teams. In this paper, we attempt to collate a list of software testing job responsibilities by applying three different social research methodologies to collect information from different sources. We found that 'test' specific responsibilities are divided into several unit tasks including test suite generation, execution of test plans, and so on. We also found that along with test specific responsibilities, software testers must perform a number of other tasks common to other IT professionals in order to carry out their roles. © 2014 IEEE.

A multi-objective evolutionary algorithm-based decision support system: a case study on job-shop scheduling in manufacturing

In this paper, an evolutionary algorithm is used for developing a decision support tool to undertake multi-objective job-shop scheduling problems. A modified micro genetic algorithm (MmGA) is adopted to provide optimal solutions according to the Pareto optimality principle in solving multi-objective optimisation problems. MmGA operates with a very small population size to explore a wide search space of function evaluations and to improve the convergence score towards the true Pareto optimal front. To evaluate the effectiveness of the MmGA-based decision support tool, a multi-objective job-shop scheduling problem with actual information from a manufacturing company is deployed. The statistical bootstrap method is used to evaluate the experimental results, and compared with those from the enumeration method. The outcome indicates that the decision support tool is able to achieve those optimal solutions as generated by the enumeration method. In addition, the proposed decision support tool has advantage of achieving the results within a fraction of the time.