Prevention of Postpartum Depression in Low-Income Women: Development of the Mamás y Bebés/Mothers and Babies Course

A prenatal intervention designed to prevent the onset of major depressive episodes (MDEs) during pregnancy and postpartum was pilot tested at a public sector women’s clinic. The Mamás y Bebés/Mothers and Babies Course is an intervention developed in Spanish and English that uses a cognitive-behavioral mood management framework, and incorporates social learning concepts, attachment theory, and socio-cultural issues. The four goals of this project were to develop the intervention, assess its acceptability, test the feasibility of conducting a randomized trial with public sector patients, and obtain estimates of its effect size. Forty-one pregnant women at high risk for developing MDEs were randomized to the Mothers and Babies Course (n = 21) or a comparison condition (n = 20). Assessments occurred during pregnancy and at 1, 3, 6, and 12 months postpartum. Differences in terms of depression symptom levels or incidence of MDEs between the two groups did not reach statistical significance in this pilot trial. However, the MDE incidence rates of 14% for the intervention condition versus 25% for the comparison condition represent a small effect size (h = 0.28) that will be further examined in a larger scale study. The intervention was well received by the participants and implementation of a randomized trial appeared quite feasible as indicated by our follow-up rate of 91% at 12 months. Implications for the continuing development of preventive interventions for perinatal depression are discussed.     

Interpersonal factors and perinatal depressive symptomatology in a low-income Latina sample

Interpersonal factors are among the risk factors that predispose women to experiencing mood disturbances during the childbearing years. This study investigates the trajectory of change in depressive symptomatology over the course of the perinatal period as related to interpersonal risk factors (marital quality and social support) in a sample of 69 low-income, mostly immigrant Latina mothers at high and low risk for depression. We found a significant linear change in depressive symptomatology from baseline (pregnancy) through the postpartum period. This decline was steeper for high-risk women who reported high levels of social support compared with those who reported low levels of social support. In addition, a greater decline in depressive symptom scores was found for women who reported better postnatal marital quality, irrespective of risk group status. The results suggest the importance of considering marital quality and social support in estimations of risk for depression. These findings also have implications for targeting social support and marital quality in preventive interventions for perinatal depression in Latinas.     

New algorithms assessing short summaries in expository texts using latent semantic analysis

In this study, we compared four expert graders with latent semantic analysis (LSA) to assess short summaries of an expository text. As is well known, there are technical difficulties for LSA to establish a good semantic representation when analyzing short texts. In order to improve the reliability of LSA relative to human graders, we analyzed three new algorithms by two holistic methods used in previous research (León, Olmos, Escudero, Cañas, &; Salmerón, 2006). The three new algorithms were (1) the semantic common network algorithm, an adaptation of an algorithm proposed by W. Kintsch (2001, 2002) with respect to LSA as a dynamic model of semantic representation; (2) a best-dimension reduction measure of the latent semantic space, selecting those dimensions that best contribute to improving the LSA assessment of summaries (Hu, Cai, Wiemer-Hastings, Graesser, &; McNamara, 2007); and (3) the Euclidean distance measure, used by Rehder et al. (1998), which incorporates at the same time vector length and the cosine measures. A total of 192 Spanish middle-grade students and 6 experts took part in this study. They read an expository text and produced a short summary. Results showed significantly higher reliability of LSA as a computerized assessment tool for expository text when it used a best-dimension algorithm rather than a standard LSA algorithm. The semantic common network algorithm also showed promising results.