Validation of an Attributional and Distributive Justice Mediational Model on the Effects of Surface Acting on Emotional Exhaustion: An Experimental Study.

Previous research has shown that surface acting-displaying an emotion that is dissonant with inner feelings-negatively impacts employees' well-being. However, most studies have neglected the meaning that employees develop around emotional demands requiring surface acting. This study examined how employees' responsibility attributions of client behavior demanding surface acting influence employees' emotional exhaustion, and the mediational role of distributive justice in this relationship. Relying on Fairness Theory, it was expected that employees' responsibility attributions of client behavior demanding emotion regulation would be related to their perceptions of distributive injustice during the service encounter, which in turn would mediate the effects of responsibility attribution on emotional exhaustion. In addition, drawing on the conservation of resources model, we contended that leader support would moderate the impact of distributive injustice on emotional exhaustion. Two scenario-based experiments were conducted. Study 1 (N = 187) manipulated the attribution of responsibility for emotional demands. The findings showed that distributive injustice and emotional exhaustion were higher when responsibility for the surface acting demands was attributed to the client. A bootstrapping mediational analysis confirmed employees' attributions have an indirect effect on emotional exhaustion through distributive justice. Study 2 (N = 227) manipulated responsibility attribution and leader support. The leader support moderation effect was confirmed.

On the new metrics for IMRT QA verification.

PURPOSE: The aim of this work is to search for new metrics that could give more reliable acceptance/rejection criteria on the IMRT verification process and to offer solutions to the discrepancies found among different conventional metrics. Therefore, besides conventional metrics, new ones are proposed and evaluated with new tools to find correlations among them. These new metrics are based on the processing of the dose-volume histogram information, evaluating the absorbed dose differences, the dose constraint fulfillment, or modified biomathematical treatment outcome models such as tumor control probability (TCP) and normal tissue complication probability (NTCP). An additional purpose is to establish whether the new metrics yield the same acceptance/rejection plan distribution as the conventional ones. METHODS: Fifty eight treatment plans concerning several patient locations are analyzed. All of them were verified prior to the treatment, using conventional metrics, and retrospectively after the treatment with the new metrics. These new metrics include the definition of three continuous functions, based on dose-volume histograms resulting from measurements evaluated with a reconstructed dose system and also with a Monte Carlo redundant calculation. The 3D gamma function for every volume of interest is also calculated. The information is also processed to obtain ΔTCP or ΔNTCP for the considered volumes of interest. These biomathematical treatment outcome models have been modified to increase their sensitivity to dose changes. A robustness index from a radiobiological point of view is defined to classify plans in robustness against dose changes. RESULTS: Dose difference metrics can be condensed in a single parameter: the dose difference global function, with an optimal cutoff that can be determined from a receiver operating characteristics (ROC) analysis of the metric. It is not always possible to correlate differences in biomathematical treatment outcome models with dose difference metrics. This is due to the fact that the dose constraint is often far from the dose that has an actual impact on the radiobiological model, and therefore, biomathematical treatment outcome models are insensitive to big dose differences between the verification system and the treatment planning system. As an alternative, the use of modified radiobiological models which provides a better correlation is proposed. In any case, it is better to choose robust plans from a radiobiological point of view. The robustness index defined in this work is a good predictor of the plan rejection probability according to metrics derived from modified radiobiological models. The global 3D gamma-based metric calculated for each plan volume shows a good correlation with the dose difference metrics and presents a good performance in the acceptance/rejection process. Some discrepancies have been found in dose reconstruction depending on the algorithm employed. Significant and unavoidable discrepancies were found between the conventional metrics and the new ones. CONCLUSIONS: The dose difference global function and the 3D gamma for each plan volume are good classifiers regarding dose difference metrics. ROC analysis is useful to evaluate the predictive power of the new metrics. The correlation between biomathematical treatment outcome models and the dose difference-based metrics is enhanced by using modified TCP and NTCP functions that take into account the dose constraints for each plan. The robustness index is useful to evaluate if a plan is likely to be rejected. Conventional verification should be replaced by the new metrics, which are clinically more relevant.

Monte Carlo modeling of the Siemens Optifocus multileaf collimator.

We have developed a new component module for the BEAMnrc software package, called SMLC, which models the tongue-and-groove structure of the Siemens Optifocus multileaf collimator. The ultimate goal is to perform accurate Monte Carlo simulations of the IMRT treatments carried out with Optifocus. SMLC has been validated by direct geometry checks and by comparing quantitatively the results of simulations performed with it and with the component module VARMLC. Measurements and Monte Carlo simulations of absorbed dose distributions of radiation fields sensitive to the tongue-and-groove effect have been performed to tune the free parameters of SMLC. The measurements cannot be accurately reproduced with VARMLC. Finally, simulations of a typical IMRT field showed that SMLC improves the agreement with experimental measurements with respect to VARMLC in clinically relevant cases. PACS NUMBER: 87.55. K.