E
Elizabeth M. Ginexi
Researcher at National Institutes of Health
Publications - 20
Citations - 619
Elizabeth M. Ginexi is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Psychological intervention & Prevention science. The author has an hindex of 11, co-authored 18 publications receiving 561 citations. Previous affiliations of Elizabeth M. Ginexi include Westat & George Washington University.
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Natural Disaster and Depression: A Prospective Investigation of Reactions to the 1993 Midwest Floods
TL;DR: Overall, the disaster led to true but small rises in depressive symptoms and diagnoses 60–90 days postflood, but increases in symptoms as a function of flood impact were slightly greater among respondents with the lowest incomes and among residents living in small rural communities, as opposed to on farms or in cities.
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A Call to Address Complexity in Prevention Science Research
TL;DR: A systems science approach to problem solving and methods especially equipped to handle complex relationships and their evolution over time are introduced and how to complement traditional methods and augment research objectives by applying systems science methodologies is proposed.
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What's next for translation research?
TL;DR: In this article, the authors explore factors contributing to the pace of translation and reaffirm that research advances or retreats the progress of scientific discovery as data accumulate in what can be described as a translational research loop that is iterative and bidirectional.
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Depression and control beliefs in relation to reemployment: what are the directions of effect?
TL;DR: In this article, individual growth curve modeling and discrete-time survival analyses were used to examine whether reemployment resolved depressive symptoms or affected control beliefs, whether depressive symptom or control beliefs predicted time to reemployment, and if these relationships changed over time.
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Handling missing data in randomized experiments with noncompliance.
TL;DR: This paper shows that identification and estimation of causal treatment effects considering both noncompliance and missing outcomes can be relatively easily conducted under various missing data assumptions, including the latent ignorability proposed by Frangakis and Rubin.