A Paradigm Shift in Predoctoral Dental Curricula in Brazil: Evaluating the Process of Change

In 2002, the Brazilian Ministry of Education approved the official curricular guidelines for undergraduate courses in Brazil to be adopted by the nation's 188 dental schools. In 2005-06, the Brazilian Dental Education Association (BDEA) promoted workshops in forty-eight of the schools to verify the degree of transformation of the curriculum based on these guidelines. Among the areas analyzed were course philosophy (variables were v1: knowledge production based on the needs of the Brazilian Public Health System [BPHS]; v2: health determinants; and v3: postgraduate studies and permanent education); pedagogical skills (v4: curricular structure; v5: changes in pedagogic and didactic skills; and v6: course program orientation); and dental practice scenarios (v7: diversity of the scenarios for training/learning; v8: academic health care centers opened to the BPHS; and v9: participation of students in health care delivery for the population). The subjects consisted of faculty members (n=711), students (n=228), and employees (n=14). The results showed an incipient degree of curriculum transformation. The degree of innovation was statistically different depending on the type of university (public or private) for variables I, 2, 4, 5, 6, and 7. Private schools reported a higher level of innovation than public institutions. Resistance to transforming the dental curriculum according to the official guidelines may be linked to an ideological conception that supports the private practice model, continues to have faculty members direct all classroom activities, and prevents students from developing an understanding of professional practice as targeted towards the oral health needs of all segments of society.

Bayesian online algorithms for learning in discrete Hidden Markov Models

We propose and analyze two different Bayesian online algorithms for learning in discrete Hidden Markov Models and compare their performance with the already known Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalization we draw learning curves in simplified situations for these algorithms and compare their performances.