Healthcare and preventive services utilization of elderly Europeans with depressive symptoms

BACKGROUND: Depressive symptoms are associated with increased healthcare utilization. However, it is unclear whether depressed individuals experience more or less frequent access to preventive services. Our goal was to investigate the association between depressive symptoms and both utilization of healthcare and preventive services. METHODS: Baseline self-reported data (2004) from non-institutionalized individuals aged >/=50 years participating in the Survey of Health, Ageing, and Retirement in Europe (SHARE) were used. Of the 18,560 respondents to the baseline questionnaire, 13,580 answered the supplementary questionnaire, which included measures of preventive services. Healthcare utilization during the previous 12 months, including outpatient visits, medication, hospitalization, surgery, and home healthcare were assessed. Preventive service measures assessed the participation in influenza immunization and colorectal and breast cancer screening. Depression status was assessed with the EURO-D, a validated instrument for which a score >3 defines clinically significant depressive symptoms. Logistic regressions were performed adjusting for age, gender, socioeconomic status, behavioral risk, chronic disease, disability, and country of residence. RESULTS: The estimated prevalence of depressive symptoms was 28.2%. Depressive symptoms were associated with significantly greater use of all healthcare domains but not preventive services, with the exception of colorectal cancer screening. Similar trends were found for each country of residence and for both genders. LIMITATIONS: It was not known whether medical tests were used for screening or diagnostic purposes. CONCLUSIONS: SHARE data suggest that patients with depressive symptoms are frequent users of healthcare but not preventive services. Low screening rates may reflect missed screening opportunities rather than a lack of screening opportunities

O Diagrama de Corpo Livre como recurso de avaliação da aprendizagem significativa da Biomecânica em um curso de Licenciatura em Educação Física

A Biomecânica é uma disciplina de natureza interdisciplinar, comumente percebida como de difícil compreensão pelos graduandos e, ao que parece, ainda pouco utilizada no cotidiano profissional de professores de Educação Física. Cientes de que congrega informações essenciais à prática desse profissional, procuramos compreender, neste estudo de caso, de abordagem qualitativa, o processo de aprendizagem significativa de conceitos necessários para a adequada elaboração do Diagrama de Corpo Livre. Os sujeitos da investigação foram os alunos da disciplina Biomecânica do curso de Licenciatura em Educação Física de uma Universidade pública do Rio de Janeiro, Brasil. Assumimos a Teoria da Aprendizagem Significativa como marco teórico e a observação participante como estratégia metodológica. Além das notas de campo, tomamos como registros as respostas de um questionário e de quatro testes realizados durante e após a disciplina que, conforme natureza, foram categorizadas. Os resultados sugeriram que, no continuum aprendizagem mecânica–significativa, a aprendizagem realizada tendia à primeira, apesar do evidente avanço do conhecimento dos alunos, o qual, em relação ao conjunto das atividades realizadas, pareceu-nos aquém do esperado para o nível do curso

A study of subgroup discovery approaches for defect prediction

Context: Although many papers have been published on software defect prediction techniques, machine learning approaches have yet to be fully explored. Objective: In this paper we suggest using a descriptive approach for defect prediction rather than the pre-cise classification techniques that are usually adopted. This allows us to characterise defective modules with simple rules that can easily be applied by practitioners and deliver a practical (or engineering) approach rather than a highly accurate result. Method: We describe two well-known subgroup discovery algorithms, the SD algorithm and the CN2-SD algorithm to obtain rules that identify defect prone modules. The empirical work is performed with pub-licly available datasets from the Promise repository and object-oriented metrics from an Eclipse reposi-tory related to defect prediction. Subgroup discovery algorithms mitigate against characteristics of datasets that hinder the applicability of classification algorithms and so remove the need for preprocess-ing techniques. Results: The results show that the generated rules can be used to guide testing effort in order to improve the quality of software development projects. Such rules can indicate metrics, their threshold values and relationships between metrics of defective modules. Conclusions: The induced rules are simple to use and easy to understand as they provide a description rather than a complete classification of the whole dataset. Thus this paper represents an engineering approach to defect prediction, i.e., an approach which is useful in practice, easily understandable and can be applied by practitioners.ICEBERG IAPP-2012-324356MICINN TIN2011-28956-C02-0

Avaliação de ferramentas computacionais utilizadas na simulação do processo de retirada dos trabalhadores, em situação de emergência

Uma instalação industrial apresenta vários riscos em decorrência do tipo de processo a ser controlado, químico, termodinâmico, nuclear, e dos perigos associados a esses processos, fogo, explosão, vazamento radioativo e de gás. O plano de emergência constitui um conjunto de regras e procedimentos destinados a evitar ou minimizar os efeitos de acidentes, catástrofes em determinadas áreas, possibilitando o gerenciamento de forma otimizada dos recursos disponíveis. A identificação das melhores rotas de fugas e a estimativa do tempo necessário para a retirada dos trabalhadores do local de risco são itens importantes que devem ser considerados na confecção de um bom plano de retirada de emergência. Este relatório tem como objetivo apresentar a metodologia e os resultados do processo de avaliação de três ferramentas computacionais de simulação, utilizadas na determinação do tempo necessário para retirada dos trabalhadores do local de trabalho, em situações de emergência

Patient Perspectives of the Doctor-at-Home Service

Introduction. Home health care has been established as an effective model for reducing mortality in the elderly. The Doctor-at-Home Service at the Community Health Centers of Burlington (CHCB) has offered home health care to Burlington residents since January 2015. Dr. Karen Sokol, MD, alone provides care to 176 patients at their homes, including at-home palliative care. CHCB hope to expand this program by hiring more providers. Objective. To understand the impact of the Doctor-at-Home Service from the pa- tients’ perspective. Methods. A survey was administered to a cohort of eighteen patients over an 8- week period and addressed topics such as barriers to healthcare, benefits, and costs associated with doctor-at-home programs. A theme analysis on the responses was then conducted to reflect patient opinions. Available summary data describing the pa- tient population was also analyzed. Results. The Doctor- at- Home program serves patients ranging from 26 to 100 years old, with the majority of the patient population comprised of senior citizens. Prior to at home care, patients faced barriers such as lack of transportation, negative past experi- ences, anxiety, and distance from relatives. Four main themes from patient responses were physician-patient relationship, convenience, quality of care, and environment of care. Discussion. Evidence is compelling that there is a desire and need for an exten- sion of the Doctor-at-Home program to reach additional patients. Doctor-at-Home pro- grams could eliminate identified barriers and provide quality care to patients, especially those with specific barriers to access.https://scholarworks.uvm.edu/comphp_gallery/1256/thumbnail.jp