Evaluation of massive education in prison health: a perspective of health care for the person deprived of freedom in Brazil.

Education, with an emphasis on prison health, has acted as a policy inducing changes in work processes, which the Brazilian National Health System (SUS) has used, and which is present in permanent health education, which promotes health care for people deprived of liberty. This article aims to present an analysis of the impacts of the strategy of massive education on prison health in Brazil from the perspective of health professionals and other actors operating in the Brazilian prison system. The data used in the study come from a questionnaire consisting of 37 questions applied nationwide between March and June 2022. Responses were collected from students who completed the course "Health Care for People Deprived of Freedom" of the learning pathway "Prison System", available in the Virtual Learning Environment of the Brazilian Health System (AVASUS). This course was offered nationally, whose adhesion (enrollment) occurred spontaneously, i.e., the course was not a mandatory. The data collected allowed us to analyze the impacts of massive education on prison health. The study also shows that the search for the course is made by several areas of knowledge, with a higher incidence in the health area, but also in other areas, such as humanities, which also work directly with the guarantee of the rights of people deprived of liberty, which are professionals in the areas of social work, psychology, and education. The analysis based on the data suggests that the massive education mediated by technology through the courses of the learning pathway, besides disseminating knowledge-following the action plan of the 2030 Agenda of the United Nations Educational, Scientific and Cultural Organization (UNESCO)-, are an effective tool to promote resilience in response to prison health and care demands of people deprived of liberty.

Interventions of Brazil's more doctors program through continuing education for Primary Health Care.

Introduction: Brazil's More Doctors Program, in its training axis, aims to improve medical training for Primary Health Care through interventions related to the reality of the territory. The research presented here analyzed the interventions implemented by Brazil's More Doctors Program physicians, members of the Family Health Continuing Education Program, and the relationship with Primary Health Care programmatic actions. Methodology: The research conducted made use of Text and Data Mining and content analysis. In total, 2,159 reports of interventions from 942 final papers were analyzed. The analysis process was composed of the formation of the corpus; exploration of the materials through text mining; and analysis of the results by inference and interpretation. Results: It was observed that 57% of the physicians worked in the Northeast Region, which was also the region with the most interventions (66.8%). From the analysis of the bigrams, trigrams, and quadrigrams, four constructs were formed: "women's health," "child health," "chronic non-communicable diseases," and "mental health." Terms related to improving access, quality of care, teamwork, and reception were also present among the N-grams. Discussion: The interventions carried out are under the programmatic actions recommended by the Brazilian Ministry of Health for Primary Health Care, also addressing cross-cutting aspects such as Reception, Teamwork, Access Improvement, and Quality of Care, which suggests that the training experience in the Family Health Continuing Education Program reflects on the way these professionals act.

Massive health education through technological mediation: Analyses and impacts on the syphilis epidemic in Brazil.

With syphilis cases on the rise, Brazil declared an epidemic in 2016. To address the consequent public health crisis, the Ministry of Health laid out a rapid response plan, namely, the "Syphilis No!" Project (SNP), a national instrument to fight the disease which encompasses four dimensions: (a) management and governance, (b) surveillance, (c) comprehensive care, and (d) strengthening of educommunication. In the dimension of education, the SNP developed the learning pathway "Syphilis and other Sexually Transmitted Infections (STIs)" to strengthen and promote Health Education. This pathway features 54 Massive Open Online Courses (MOOCs), delivered through the Virtual Learning Environment of the Brazilian Health System (AVASUS). This paper analyzes the impacts of the learning pathway "Syphilis and other STIs" on the response to the epidemic in Brazil, highlighting the educational process of the learning pathway and its social implications from the perspective of the United Nations' 2030 Agenda and its Sustainable Development Goals. Three distinct databases were used to organize the educational data: the learning pathway "Syphilis and other STIs" from AVASUS, the National Registry of HealthCare Facilities from the Brazilian Ministry of Health (MoH), and the Brazilian Occupation Classification, from the Ministry of Labor. The analysis provides a comprehensive description of the 54 courses of the learning pathway, which has 177,732 enrollments and 93,617 participants from all Brazilian regions, especially the Southeast, which accounts for the highest number of enrollees. Additionally, it is worth noting that students living abroad also enrolled in the courses. Data characterization provided a demographic study focused on the course participants' profession and level of care practiced, revealing that the majority (85%) worked in primary and secondary healthcare. These practitioners are the target audience of the learning pathway and, accordingly, are part of the personnel directly engaged in healthcare services that fight the syphilis epidemic in Brazil.

Virtual Learning Environment of the Brazilian Health System (AVASUS): Efficiency of Results, Impacts, and Contributions.

The Virtual Learning Environment of the Brazilian Health System (AVASUS) is a free and open distance education platform of the Ministry of Health (MS). AVASUS is a scalable virtual learning environment that has surpassed 800,000 users, 2 million enrollments, and 310 courses in its catalog. The objective of this paper was to assess the impacts of the educational offerings on health services and AVASUS course participants' professional practice. This study analyzed data from AVASUS, the Brazilian National Registry of Health Care Facilities (CNES), the Brazilian Occupational Classification (CBO), and a questionnaire applied to 720-course participants from five regions of Brazil. After acquiring and extracting data, computational methods were used for the evaluation process. Only the responses of 462 participants were considered for data analysis, as they had a formal link to CNES. The results showed that respondents recommended 76.2% of AVASUS courses to peers. Accordingly, the quality of educational offerings motivated 81.3% of such recommendations. In addition, 75.6% of course participants who answered the questionnaire also indicated that AVASUS course contents contribute to enhancing existing health services in the health facilities where they work. Finally, 24.6% of all responses mentioned that courses available in AVASUS were essential in offering new health services in such facilities.

The Text Mining Technique Applied to the Analysis of Health Interventions to Combat Congenital Syphilis in Brazil: The Case of the "Syphilis No!" Project.

Congenital syphilis (CS) remains a threat to public health worldwide, especially in developing countries. To mitigate the impacts of the CS epidemic, the Brazilian government has developed a national intervention project called "Syphilis No." Thus, among its range of actions is the production of thousands of writings featuring the experiences of research and intervention supporters (RIS) of the project, called field researchers. In addition, this large volume of base data was subjected to analysis through data mining, which may contribute to better strategies for combating syphilis. Natural language processing is a form of knowledge extraction. First, the database extracted from the "LUES Platform" with 4,874 documents between 2018 and 2020 was employed. This was followed by text preprocessing, selecting texts referring to the field researchers' reports for analysis. Finally, for analyzing the documents, N-grams extraction (N = 2,3,4) was performed. The combination of the TF-IDF metric with the BoW algorithm was applied to assess terms' importance and frequency and text clustering. In total, 1019 field activity reports were mined. Word extraction from the text mining method set out the following guiding axioms from the bigrams: "confronting syphilis in primary health care;" "investigation committee for congenital syphilis in the territory;" "municipal plan for monitoring and investigating syphilis cases through health surveillance;" "women's healthcare networks for syphilis in pregnant;" "diagnosis and treatment with a focus on rapid testing." Text mining may serve public health research subjects when used in parallel with the conventional content analysis method. The computational method extracted intervention activities from field researchers, also providing inferences on how the strategies of the "Syphilis No" Project influenced the decrease in congenital syphilis cases in the territory.

Image segmentation of ovitraps for automatic counting of Aedes Aegypti eggs.

The Aedes Aegypti mosquito is the vector of the most difficult public health problems in tropical and semi-tropical world: the epidemic proliferation of dengue, a viral disease that can cause human beings death specially in its most dangerous form, dengue haemorrhagic fever. One of the most useful methods for mosquito detection and surveillance is the ovitraps: special traps to collect eggs of the mosquito. It is very important to count the number of Aedes Aegypti eggs present in ovitraps. This counting is usually performed in a manual, visual and non-automatic form. This work approaches the development of automatic methods to count the number of eggs in ovitraps images using image processing, particularly color segmentation and mathematical morphology-based non-linear filters.