Aim. The aim of the study was to test a two-level model of the relationships between structural empowerment in a hospital department and job satisfaction and burnout in nurses. We tested whether psychological empowerment is a mediator of these relationships. Background. We drew on empowerment theory to examine whether psychological empowerment mediates the association between organizational-level structural empowerment and nurses’ job satisfaction and burnout at the individual level. The proposed two-level model takes into account the effect of both contextual and individual factors on individual nurse’s job satisfaction and burnout. Methods. The study was conducted in 29 public hospital departments and included 309 participants employed as nurses or midwives. To analyze the results, we applied multilevel modeling and cross-level mediation analysis, with nurses as Level 1 and hospital departments as Level 2. Results. Structural empowerment at the hospital department level is positively related to nurses’ individual sense of competence and autonomy, namely, to their psychological empowerment. Structural empowerment is also positively related to job satisfaction and negatively related to burnout in nurses. Psychological empowerment is a mediator between structural empowerment and nurses’ job satisfaction as well as two dimensions of burnout: exhaustion and disengagement from work. Conclusions. These findings suggest that psychological empowerment is an underlying mechanism that may explain why structural empowerment in the hospital department is positively related to job satisfaction and negatively related to burnout in nurses. This has implications for theory by extending the multilevel nomological network of the constructs and for management practice by highlighting the role of structural empowerment for work design in public health institutions. Implications for Nursing Management. The results indicate that structural and psychological empowerment can play a significant role in creating supportive workplace conditions in hospitals. Organizing nurses’ work in a way that empowers them promotes their sense of competence and autonomy, which in turn promotes their job satisfaction and reduces burnout.

The present work proposes a simple supervised method based on a downsampled time-frequency representation of the input audio signal for detecting the presence of the queen in a beehive from noisy field recordings. Our proposed technique computes a "summarized-spectrogram" of the signal that is used as the input of a deep convolutional neural network. This approach has the advantage of reducing the dimension of the input layer and the computational cost while obtaining better classification results with the same deep neural architecture. Our comparative evaluation based on a cross-validation beehive-independent methodology shows a maximal accuracy of 96% using the proposed approach applied on the evaluation dataset. This corresponds to a significant improvement of the prediction accuracy in comparison to several state-of-the-art approaches reported by the literature. Baseline methods such as MFCC, constant-Q transform and classical STFT combined with a CNN fail to generalize the prediction of the queen presence in an unknown beehive and obtain a maximal accuracy of 55% in our experiments.