Multicenter Adaptation Of The Guide Autonomous Management Of Medication

Rising psychotropic prescriptions and low empowerment of mental health users hamper qualified delivery of mental health services in Brazil and countries providing universal healthcare. This multicentric study performed in three Brazilian cities aimed to develop the Brazilian translation and adaptation upon the Quebecoise-Canadian Gaining Autonomy and Medication (GAM) Guide; and to evaluate the impact of its use on mental health workers education. Intervention Groups (IGs) were held to provide experience sharing on medication as relevant issues were brought up by the guide. Pre- and post- IGs focus groups were held. Major changes to the original text of the Quebecoise handbook were necessary according to the Brazilian scenario. Results show the Brazilian version as a powerful strategy to promote users' participation in decision making concerning treatment as well as the mental health services they are in treatment at. 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Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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