Principais fatores relacionados à impossibilidade de amamentação em Puérperas assistidas no Isea/ Principles related fators à amamentação impossibility of em Puérperas assistidas no Isea

A amamentação é uma das etapas mais relevantes da vida reprodutiva da mulher e sua realização traz benefícios cientificamente comprovados. Apesar das vantagens, sua prática está aquém do que é preconizado pelos órgãos de saúde. Muitos são os fatores que impossibilitam a puérpera de amamentar, os principais influenciadores desses fatores são as condições socioculturais, psicológicas e físicas da mulher, bem como as condições de saúde da criança. A não ocorrência da amamentação, além de privar o binômio mãe-filho de vantagens como nutrição adequada da criança, prevenção de doenças crônicas e infecciosas; prevenção de câncer de mama e hemorragias na mulher, geralmente é acompanhada por sentimentos negativos acerca da impossibilidade de amamentar. Diante do contexto, é de fundamental importância que sejam desenvolvidas atividades que verifiquem as causas de não realização da amamentação pelas puérperas. Como objetivo, esta pesquisa propôs identificar os fatores que mais frequentemente impedem a amamentação entre as usuárias do banco de leite do ISEA. Trata-se de estudo descritivo com abordagem quantitativa. Tem-se como amostra 90 questionários que atenderam aos critérios de elegibilidade. A causa mais frequente de interdição à amamentação foi a prematuridade; 85% das mulheres afirmaram sentimentos negativos diante da impossibilidade de amamentar. Apenas 19% das participantes realizam acompanhamento psicológico. Através dos dados obtidos neste trabalho, espera-se que órgãos de saúde e entidades de ensino sejam estimulados a promover ações que atenuem os fatores que impossibilitam a amamentação. Além disso, há expectativa de maior apoio à saúde mental das mulheres que não conseguem amamentar seus filhos, reduzindo-se assim as consequências da não realização da amentação

A IMPORTÂNCIA DO PRÉ-NATAL NA PREVENÇÃO DE COMPLICAÇÕES DURANTE A GESTAÇÃO

To use scientific evidence to analyze the importance of prenatal care in preventing complications during pregnancy. Methods: This is a qualitative integrative literature review. The search for studies involved in the research was carried out in the following databases: SCIELO, LILACS, BDENF and MEDLINE, using the health sciences descriptors: "Pregnancy", "Prenatal care" and "Prevention". The inclusion criteria were: published between 2014 and 2024, with free access to full texts, articles in Portuguese, English and Spanish and related to the theme. Exclusion criteria were: duplicate articles, incomplete articles, abstracts, reviews, debates, articles published in event proceedings and unavailable in full. Results: In addition to these activities and early diagnosis during prenatal care, it is possible to carry out intrauterine treatment, which enables the baby to be properly assessed. Conclusion: It can be concluded that prenatal care is the main strategy for preventing health complications during pregnancy, and is most often carried out in primary care.Analisar por meio das evidências cientificas a importância do pré-natal na prevenção de complicações durante a gestação. Métodos: Trata-se de uma revisão integrativa da literatura de caráter qualitativo. A busca dos trabalhos envolvidos na pesquisa foi realizada nas seguintes bases de dados: SCIELO, LILACS, BDENF e MEDLINE, a partir dos descritores em ciências da saúde: “Gravidez”, “Pré-natal” e “Prevenção”. Os critérios de inclusão foram: publicados no período entre 2014 e 2024, cujo acesso ao periódico era livre aos textos completos, artigos em idioma português, inglês e espanhol e relacionados a temática. Critérios de exclusão foram: artigos duplicados, incompletos, resumos, resenhas, debates, artigos publicados em anais de eventos e indisponíveis na íntegra. Resultados: Além dessas atividades e do diagnóstico precoce no pré-natal é possível a realização do tratamento intra-uterino que possibilita uma avaliação adequado do bebê. Conclusão: Conclui-se que o pré-natal é a principal estratégia para a prevenção de complicações de saúde durante a gestação, sendo realizada com mais frequência na atenção primária

Evaluation Of The Overload Of Care In Families Of Psychiatric Patients In Psychosocial Care Center

Introduction: The burden of care in family refers to the weight caused by the primary caregiver role to psychiatric patients and the difficulties encountered in performing this function in daily life. Objectives: Assessing the objective and subjective overload of family members who live with the reality of psychiatric disorder in a child day-care psychosocial care center. Methods: Cross-sectional study, descriptive-exploratory, of quantitative approach, with non-probabilistic samples of accidental type with 80 families of psychiatric patients held in a Psychosocial Care Center. For overload evaluation, the subscales "B" and "D" of the Family Overload Rating Scale (FBIS-BR) were used. Results: The study was conducted with 80 families of psychiatric patients. The average age of female caregivers was 39,6 years old, and 40,7 years old for male caregivers, with female predominance (87,5%) compared to men (12,5%), with low education for both genres. Family caregivers presented high objective burden due to excessive demand attention (p<0,001), heteroaggressiveness (p<0,001) and perplexing behavior of psychiatric patients regarding the supervision of problematic behaviors (p<0,001). The items on the impact on the family's daily routine have not helped to generate objective overload for the family members. On subjective overload, it was clear to observe familiar members with high degree of disturbance in all the dimensions assessed (p < 0,001). Conclusion: The high degree of care overload observed in family members indicates the need to develop contacts with the family of the psychiatric patient to answer questions, offer support and assistance to the family caregiver. Keywords: Caregivers. Patients. Mental Health Services

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost