Organisational and professional commitments: the influence in nurses´organizational citizenship behaviours

The way an employee behaves in his work can be influenced by the organisational and professional commitment. Nurses are professionals who are guided by organisational and professional goals and values. Among nurses, professional commitment may be an important antecedent of organisational citizenship behaviours. The study shows how organisational and professional commitment is related with nurses’ organisational citizenship behaviours. Data from a sample of 420 nurses working in two hospitals --- the Hospital of St. Marcos, Braga and the Hospital Centre of Alto Ave, Guimarães and Fafe units were collected. The main findings are as follows: (a) organisational commitment and professional commitment contribute to the explanation of nurses’ organisational citizenship behaviours, (b) affective organisational commitment, continuance organisational commitment --- personal sacrifice, affective professional commitment and continuance professional commitment explain 28.6% of variance of organisational citizenship behaviours. © 2013 Instituto Politécnico do Cávado e do Ave (IPCA). Published by Elsevier España, S.L.U. All rights reserved.

Computer-aided recognition of dental implants in X-ray images

Dental implant recognition in patients without available records is a time-consuming and not straightforward task. The traditional method is a complete user-dependent process, where the expert compares a 2D X-ray image of the dental implant with a generic database. Due to the high number of implants available and the similarity between them, automatic/semi-automatic frameworks to aide implant model detection are essential. In this study, a novel computer-aided framework for dental implant recognition is suggested. The proposed method relies on image processing concepts, namely: (i) a segmentation strategy for semi-automatic implant delineation; and (ii) a machine learning approach for implant model recognition. Although the segmentation technique is the main focus of the current study, preliminary details of the machine learning approach are also reported. Two different scenarios are used to validate the framework: (1) comparison of the semi-automatic contours against implant’s manual contours of 125 X-ray images; and (2) classification of 11 known implants using a large reference database of 601 implants. Regarding experiment 1, 0.97±0.01, 2.24±0.85 pixels and 11.12±6 pixels of dice metric, mean absolute distance and Hausdorff distance were obtained, respectively. In experiment 2, 91% of the implants were successfully recognized while reducing the reference database to 5% of its original size. Overall, the segmentation technique achieved accurate implant contours. Although the preliminary classification results prove the concept of the current work, more features and an extended database should be used in a future work.