Effectiveness of a mindfulness-based childbirth education pilot study on maternal self-efficacy and fear of childbirth

Introduction: This pilot study tested the feasibility and effectiveness of using Mindfulness-Based Childbirth Education (MBCE), a novel integration of mindfulness meditation and skills-based childbirth education, for mental health promotion with pregnant women. The MBCE protocol aimed to reduce fear of birth, anxiety, and stress and improve maternal self-efficacy. This pilot study also aimed to determine the acceptability and feasibility of the MBCE protocol. Methods: A single-arm pilot study of the MBCE intervention using a repeated-measures design was used to analyze data before and after the MBCE intervention to determine change trends with key outcome variables: mindfulness; depression, anxiety, and stress; childbirth self-efficacy; and fear of childbirth. Pregnant women (18-28 weeks’ gestation) and their support companions attended weekly MBCE group sessions over 8 weeks in an Australian community setting.Results: Of the 18 women who began and completed the intervention, missing data allowed for complete data from12 participants to be analyzed. Statistically significant improvements and large effect sizes were observed for childbirth self-efficacy and fear of childbirth. Improvements in depression, mindfulness, and birth outcome expectations were underpowered. At postnatal follow-up significant improvements were found in anxiety, whereas improvements in mindfulness, stress, and fear of birth were significant at a less conservative alpha level. Discussion: This pilot study demonstrated that a blended mindfulness and skills-based childbirth education intervention was acceptable to women and was associated with improvements in women’s sense of control and confidence in giving birth. Previous findings that low self-efficacy and high childbirth fear are linked to greater labor pain, stress reactivity, and trauma suggest the observed improvements in these variables have important implications for improving maternal mental health and associated child health outcomes. Ways in which these outcomes can be achieved through improved childbirth education are discussed

A study on using genetic niching for query optimisation in document retrieval

International audienceThis paper presents a new genetic approach for query optimisation in document retrieval. The main contribution of the paper is to show the effectiveness of the genetic niching technique to reach multiple relevant regions of the document space. Moreover, suitable merging procedures have been proposed in order to improve the retrieval evaluation. Experimental results obtained using a TREC sub-collection indicate that the proposed approach is promising for applications

A atuação do BNDES na Amazônia Legal

Inclui bibliografia: p. 23-24 e notas de rodapéConteúdo sob licença Creative Commons Atribuição 4.0 Internacional. Os textos desta publicação podem ser reproduzidos no todo ou em parte desde que a fonte e os respectivos autores sejam citados.(Amazônia 2030 ; 45)O Banco Nacional de Desenvolvimento Econômico e Social (BNDES) é o principal instrumento do Governo Federal para o financiamento de longo prazo e investimento no Brasil. Além disso, o Banco contribui para a promoção do desenvolvimento econômico e social do país e tem o objetivo de ser o banco do desenvolvimento sustentável brasileiro. No entanto, sua atuação não é uniforme entre as regiões brasileiras. A região Norte é a região que possui a maior participação dos desembolsos em relação ao seu PIB. Porém, detém a menor proporção dos desembolsos totais do BNDES no Brasil. Dessa forma, pode-se indicar que o BNDES desempenha um importante papel de banco de fomento para o Norte, ainda que a região não possua o mesmo destaque financeiro para o Banco

Efeito de formulações de iscas tóxicas sobre Anastrepha fraterculus e Ceratitis capitata (Diptera: Tephritidae).

(Embrapa Uva e Vinho. Documentos, 99

Kinetics of the urea–urease clock reaction with urease immobilized in hydrogel beads

Feedback driven by enzyme catalyzed reactions occurs widely in biology and has been well characterized in single celled organisms such as yeast. There are still few examples of robust enzyme oscillators in vitro that might be used to study nonlinear dynamical behavior. One of the simplest is the urea–urease reaction that displays autocatalysis driven by the increase in pH accompanying the production of ammonia. A clock reaction was obtained from low to high pH in batch reactor and bistability and oscillations were reported in a continuous flow rector. However, the oscillations were found to be irreproducible and one contributing factor may be the lack of stability of the enzyme in solution at room temperature. Here, we investigated the effect of immobilizing urease in thiol-poly(ethylene glycol) acrylate (PEGDA) hydrogel beads, prepared using emulsion polymerization, on the urea–urease reaction. The resultant mm-sized beads were found to reproduce the pH clock and, under the conditions employed here, the stability of the enzyme was increased from hours to days

Machine Learning in Automated Text Categorization

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert manpower, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey