O enfermeiro e a estratégia saúde da família: desafios em coordenar a equipe multiprofissional

This study aimed at understanding the role played by nurses in managing a team in the Family Health Strategy (ESF) in relation to competences and skills practiced and developed in their everyday work and the difficulties found to perform their duties based on these professionals’ perceptions. Data were collected by means of focus groups with seven nurses in 2006 and then submitted to content analysis, which disclosed five themes: The context of the coordination of multidisciplinary teams in the Family Health Strategy, factors involved in the daily work in the ESF, conflicts experienced in the interface between teamwork and central coordination in the ESF, difficulties of the population towards the new model of care coordinated by the nurse, the perceived competence to exercise leadership in coordinating multidisciplinary teams. Results showed work overload, overlapping of tasks and lack of training. These professionals have been evaluated according to the logic of their work organization by productivity and not by the quality of their actions. Hence, they feel devalued professionally. They pointed out technical and scientific knowledge as an important competence attached to relational practices. There is a need to create formal opportunities to discuss the major difficulties found by the nurses managing multiprofessional teams when experiencing such management.

Particle competition and cooperation to prevent error propagation from mislabeled data in semi-supervised learning

Semi-supervised learning is applied to classification problems where only a small portion of the data items is labeled. In these cases, the reliability of the labels is a crucial factor, because mislabeled items may propagate wrong labels to a large portion or even the entire data set. This paper aims to address this problem by presenting a graph-based (network-based) semi-supervised learning method, specifically designed to handle data sets with mislabeled samples. The method uses teams of walking particles, with competitive and cooperative behavior, for label propagation in the network constructed from the input data set. The proposed model is nature-inspired and it incorporates some features to make it robust to a considerable amount of mislabeled data items. Computer simulations show the performance of the method in the presence of different percentage of mislabeled data, in networks of different sizes and average node degree. Importantly, these simulations reveals the existence of the critical points of the mislabeled subset size, below which the network is free of wrong label contamination, but above which the mislabeled samples start to propagate their labels to the rest of the network. Moreover, numerical comparisons have been made among the proposed method and other representative graph-based semi-supervised learning methods using both artificial and real-world data sets. Interestingly, the proposed method has increasing better performance than the others as the percentage of mislabeled samples is getting larger. © 2012 IEEE.