Promoting secure attachment: evaluation of the effectiveness of an early intervention pilot programme with mother-infant dyads in Santiago, Chile.

BackgroundResearch indicates that the early attachment patterns of babies could influence their socio-emotional development and prevent the emergence of problematic behaviours in the child later in life. Many studies in the field of early attachment interventions have promoted a secure attachment bond between mother and infant. The purpose of this study was to evaluate the effectiveness of an early pilot intervention programme designed to promote a secure attachment bond in mother-infant dyads belonging to a population seeking regular treatment at urban health centres in Santiago, Chile.MethodsPrimipara mothers were randomly assigned to two intervention conditions: a secure attachment promotion programme (experimental group = 43) or an educational talk (control group = 29). The Strange Situation Assessment was used to collect data on the attachment patterns of babies.ResultsThe results show that after the intervention, there were more babies with secure attachment in the experimental group than in the control group.ConclusionsThese findings represent a preliminary step towards evaluating interventions aimed at promoting secure attachment in Chilean mother-child dyads. While the effect of the intervention is not significant, the effect size obtained is respectable and consistent with other meta-analytic findings.

Geomodelling of a fluvial system with semi-supervised support vector regression

Fluvial deposits are a challenge for modelling flow in sub-surface reservoirs. Connectivity and continuity of permeable bodies have a major impact on fluid flow in porous media. Contemporary object-based and multipoint statistics methods face a problem of robust representation of connected structures. An alternative approach to model petrophysical properties is based on machine learning algorithm ? Support Vector Regression (SVR). Semi-supervised SVR is able to establish spatial connectivity taking into account the prior knowledge on natural similarities. SVR as a learning algorithm is robust to noise and captures dependencies from all available data. Semi-supervised SVR applied to a synthetic fluvial reservoir demonstrated robust results, which are well matched to the flow performance