Aspectos evolutivos da depressão pós-parto e fatores associados

A depressão pós-parto (DPP) é uma condição prevalente que afeta globalmente as mulheres puérperas. Uma hipótese evolutiva aborda a depressão, e consequentemente a DPP, como uma resposta proveniente da evolução do comportamento humano ao longo da História, através da seleção natural. A teoria do investimento parental sugere que os pais não investem automaticamente em toda prole; o investimento é direcionado para que o sucesso reprodutivo seja máximo. No caso de os riscos superarem os benefícios reprodutivos, sintomas de depressão se desenvolvem como sinal de alerta. O objetivo do estudo foi identificar fatores associados à DPP que fossem compatíveis com a teoria do investimento parental. Estudo transversal realizado com 811 mães de lactentes até cinco meses de idade, no município do Rio de Janeiro. A presença de DPP foi definida com base no escore da Escala de Edinburgh (EPDS). Fatores potencialmente associados à DPP foram analisados através de regressão logística com ajuste para fatores de confundimento. Os fatores significativamente associados à DPP foram: apoio social inadequado (OR 3,38; IC 95% 2,32-4,94), baixa escolaridade (OR 2,82; IC 95% 1,69-4,70), violência física entre parceiros íntimos na gestação (OR 2,33; IC 95% 1,56-3,47), idade materna inferior a 35 anos (OR 2,20; IC 95% 1,05-4,64), falta de companheiro (OR 1,90; IC 95% 1,16-3,12), internações durante a gestação (OR 1,87; IC 95% 1,12-3,14) e prematuridade do recém-nascido (OR 1,87; IC 95% 1,02-3,42). Em suma, identificamos alguns fatores associados à DPP que podem ser úteis no rastreamento e acompanhamento de mulheres de risco. Alguns dos fatores associados à DPP podem ser explicados através das hipotéses evolutivas contempladas neste estudo. Entretanto, os achados encontrados não são suficientes para esgotar o conhecimento referente a esta questão. Futuras pesquisas devem focar em diferentes abordagens desta condição e acompanhamento das consequências para as mulheres e suas famílias.

Computer system support for data analysis

This thesis is an investigation into the nature of data analysis and computer software systems which support this activity.

The first chapter develops the notion of data analysis as an experimental science which has two major components: data-gathering and theory-building. The basic role of language in determining the meaningfulness of theory is stressed, and the informativeness of a language and data base pair is studied. The static and dynamic aspects of data analysis are then considered from this conceptual vantage point. The second chapter surveys the available types of computer systems which may be useful for data analysis. Particular attention is paid to the questions raised in the first chapter about the language restrictions imposed by the computer system and its dynamic properties.

The third chapter discusses the REL data analysis system, which was designed to satisfy the needs of the data analyzer in an operational relational data system. The major limitation on the use of such systems is the amount of access to data stored on a relatively slow secondary memory. This problem of the paging of data is investigated and two classes of data structure representations are found, each of which has desirable paging characteristics for certain types of queries. One representation is used by most of the generalized data base management systems in existence today, but the other is clearly preferred in the data analysis environment, as conceptualized in Chapter I.

This data representation has strong implications for a fundamental process of data analysis -- the quantification of variables. Since quantification is one of the few means of summarizing and abstracting, data analysis systems are under strong pressure to facilitate the process. Two implementations of quantification are studied: one analagous to the form of the lower predicate calculus and another more closely attuned to the data representation. A comparison of these indicates that the use of the "label class" method results in orders of magnitude improvement over the lower predicate calculus technique.

Religious Coping, Social Support and Subjective Severity as Predictors of Posttraumatic Growth in People Affected by the Earthquake in Chile on 27/2/2010

The aim of this research is to study the impact of religious coping, social support and subjective severity on Posttraumatic Growth (PTG) in people who lost their homes after the earthquake in Chile in 2010 and who now live in transitional shelters. One hundred sixteen adult men and women were evaluated using a subjective severity scale, the Posttraumatic Growth Inventory (PTGI), the Multidimensional Scale of Perceived Social Support (MSPSS) scale of social support and the Brief RCOPE scale of religious coping. The multiple linear regression analysis shows that social support and positive religious coping have an impact on PTG. On using a bootstrap estimate, it was found that positive religious coping fully mediates the relationship between subjective severity and PTG.