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Protected areas currently cover about 15% of the global land area, and constitute one of the main tools in biodiversity conservation. Quantifying their effectiveness at protecting species from local decline or extinction involves comparing protected with counterfactual unprotected sites representing “what would have happened to protected sites had they not been protected”. Most studies are based on pairwise comparisons, using neighbour sites to protected areas as counterfactuals, but this choice is often subjective and may be prone to biases. An alternative is to use large-scale biodiversity monitoring datasets, whereby the effect of protected areas is analysed statistically by controlling for landscape differences between protected and unprotected sites, allowing a more targeted and clearly defined measure of the protected areas effect. Here we use the North American Breeding Bird Survey dataset as a case study to investigate the effectiveness of protected areas at conserving bird assemblages. We analysed the effect of protected areas on species richness, on assemblage-level abundance, and on the abundance of individual species by modelling how these metrics relate to the proportion of each site that is protected, while controlling for local habitat, altitude, productivity and for spatial autocorrelation. At the assemblage level, we found almost no relationship between protection and species richness or overall abundance. At the species level, we found that forest species are present in significantly higher abundances within protected forest sites, compared with unprotected forests, with the opposite effect for species that favour open habitats. Hence, even though protected forest assemblages are not richer than those of unprotected forests, they are more typical of this habitat. We also found some evidence that species that avoid human activities tend to be favoured by protection, but found no such effect for regionally declining species. Our results highlight the complexity of assessing protected areas effectiveness, and the necessity of clearly defining the metrics of effectiveness and the controls used in such assessments.

BackgroundLoss of a spouse is a frequent occurrence in later life. While most older adults successfully process this loss and will return to a normal life, about 10% of the individuals are unable to cope, and progress to prolonged grief (PG). PG, in turn, can result in mental and physical problems including poor sleep, cardiovascular problems, depression, and suicidal tendencies. ObjectiveLEAVES (optimizing the mentaL health and resiliencE of older Adults that haVe lost thEir spouSe via blended, online therapy) is an online bereavement program that will support the prevention and treatment of PG, so that elderly mourners can continue to lead an active, meaningful, and dignified life. LEAVES will cater to secondary end users (eg, family, informal caregivers) by reducing stress. MethodsLEAVES will help older adults to process the loss of a spouse in an online environment, which consists of (1) an existing online grief self-help program LIVIA, (2) the Before You Leave program that allows for storing personal memories, (3) a virtual agent platform, and (4) an accessible front-end design. LEAVES can detect persons at risk for complications, reveal negative trends in their emotional life, and act to counter such trends. The service relies on online support whenever possible but is blended with telephone or face-to-face counseling when necessary. ResultsThe project will take place between February 2020 and January 2023 and includes a real-life evaluation in which 315 end users will use the service across 3 countries (the Netherlands, Portugal, and Switzerland). The evaluation of LEAVES will focus on clinical effect, its business case, and technology acceptance. The results will pave the way for smooth integration into existing care paths and reimbursement schemes. ConclusionsThe LEAVES service aims to soften the mourning process, prevents depression or social isolation, strengthens widow(er)s resilience and well-being, and quickens one’s return to societal participation. International Registered Report Identifier (IRRID)DERR1-10.2196/19344

PURPOSE To determine the effects of stratified primary care for low back pain (SPLIT program) in decreasing back-related disability for patients with low back pain (LBP) in primary care. METHODS We conducted a before-and-after study. We compared health-related outcomes for 2 sequential, independent cohorts of patients with LBP recruited at 7 primary care units in Portugal. The first prospective cohort study characterized usual care (UC) and collected data from February to September 2018. The second was performed when the SPLIT program was implemented and collected data from November 2018 to October 2021. Between cohorts, physical therapists were trained in the implementation of the SPLIT program, which used the STarT Back Screening Tool to categorize patients for matched treatment. We compared back-related disability (Roland-Morris Disability Questionnaire, 0-24 points), pain (Numeric Pain Rating Scale, 0-10 points), perceived effect of treatment (Global Perceived Effect Scale, -5 to +5 points), and health-related quality of life (EuroQoL 5 dimensions 3 levels index, 0-1 points). RESULTS We enrolled a total of 447 patients: 115 in the UC cohort (mostly treated with pharmacologic treatment) and 332 in the SPLIT cohort (all referred for a physical therapy intervention program). Over the study period of 6 months, patients in the SPLIT program showed significantly greater improvements in back-related disability ([beta], -2.94; 95% CI, -3.63 to -2.24; P[less than or equal to].001), pain ([beta], -0.88; 95% CI, -1.18 to -0.57; P[less than or equal to].001), perceived effect of treatment ([beta], 1.40; 95% CI, 0.97 to 1.82; P[less than or equal to].001), and health-related quality of life ([beta], 0.11; 95% CI, 0.08 to 0.14; P[less than or equal to].001) compared with UC. CONCLUSIONS Patients in the SPLIT program for LBP showed greater benefits regarding health-related outcomes than those receiving UC. https://doi.org/10.1370/afm.3104, INTRODUCTION Low back pain (LBP) is a high-burden health problem worldwide, and its management represents a serious challenge for health systems. (1-4) The burden caused by LBP is mainly explained [...]

Este estudo dedica-se à análise de relações de controle estabelecidas nas aulas de Didática. Busca compreender mecanismos subjacentes às práticas desenvolvidas em seu ensino, no curso de Pedagogia, revelando aspectos do tipo de formação que vem sendo oferecido aos futuros docentes. Foi selecionada uma instituição privada para a coleta de informações, localizada na cidade de São Paulo. Estas foram coletadas por meio de observação e organizadas contando com cenas as quais foram analisadas com o uso de instrumentos pautados no conceito de enquadramento, de Bernstein. Seus resultados se referem aos indicadores de características das práticas para a formação da consciência profissional docente. Ressalta-se o controle constante do ritmo das atividades dos alunos pelo professor e a baixíssima condição avaliativa durante as aulas quanto aos trabalhos realizados pelos alunos e às explicações docentes

Con este experimento, para alumnos de la educación secundaria, se pretende estimular el gusto por la ciencia experimental. De una manera lúdica, vamos a intentar ejemplificar el movimiento de la materia en la naturaleza e ilustrar las consecuencias que los desequilibrios en los ciclos biogeoquímicos pueden tener para los seres vivos.

ObjectiveSome patients with rheumatic diseases might be at higher risk for coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS). We aimed to develop a prediction model for COVID-19 ARDS in this population and to create a simple risk score calculator for use in clinical settings.MethodsData were derived from the COVID-19 Global Rheumatology Alliance Registry from March 24, 2020, to May 12, 2021. Seven machine learning classifiers were trained on ARDS outcomes using 83 variables obtained at COVID-19 diagnosis. Predictive performance was assessed in a US test set and was validated in patients from four countries with independent registries using area under the curve (AUC), accuracy, sensitivity, and specificity. A simple risk score calculator was developed using a regression model incorporating the most influential predictors from the best performing classifier.ResultsThe study included 8633 patients from 74 countries, of whom 523 (6%) had ARDS. Gradient boosting had the highest mean AUC (0.78; 95% confidence interval [CI]: 0.67-0.88) and was considered the top performing classifier. Ten predictors were identified as key risk factors and were included in a regression model. The regression model that predicted ARDS with 71% (95% CI: 61%-83%) sensitivity in the test set, and with sensitivities ranging from 61% to 80% in countries with independent registries, was used to develop the risk score calculator.ConclusionWe were able to predict ARDS with good sensitivity using information readily available at COVID-19 diagnosis. The proposed risk score calculator has the potential to guide risk stratification for treatments, such as monoclonal antibodies, that have potential to reduce COVID-19 disease progression.