Stressful life events and prenatal representations of the child.

Caregivers' mental representations of their children can be assessed prenatally and are prospectively associated with later caregiving quality and caregiver-child attachment. Compared to balanced, distorted or disengaged representations are linked to insecure caregiver-child attachments. The present study explored factors (i.e. stressful life experiences and positive experiences) that may be linked to risk for distorted and disengaged representations. We used a brief version of the Prenatal Working Model of the Child Interview in a sample of 298 pregnant people (ages 19 to 45 years; M = 30.83, SD = 5.00) between gestational age 11-38 weeks (M = 23.49, SD = 5.70). A greater number of stressful events across three developmental periods (i.e., lifespan, childhood, and pregnancy) were related to increased odds of distorted, compared to balanced classification. Pregnancy stress had the largest association. Positive experiences from childhood did not buffer the association between stress and representations. Findings highlight the importance of stress on prenatal representations of one's child.

Disruption Leads to Methodological and Analytic Innovation in Developmental Sciences: Recommendations for Remote Administration and Dealing With Messy Data.

The COVID-19 pandemic has impacted data collection for longitudinal studies in developmental sciences to an immeasurable extent. Restrictions on conducting in-person standardized assessments have led to disruptive innovation, in which novel methods are applied to increase participant engagement. Here, we focus on remote administration of behavioral assessment. We argue that these innovations in remote assessment should become part of the new standard protocol in developmental sciences to facilitate data collection in populations that may be hard to reach or engage due to burdensome requirements (e.g., multiple in-person assessments). We present a series of adaptations to developmental assessments (e.g., Mullen) and a detailed discussion of data analytic approaches to be applied in the less-than-ideal circumstances encountered during the pandemic-related shutdown (i.e., missing or messy data). Ultimately, these remote approaches actually strengthen the ability to gain insight into developmental populations and foster pragmatic innovation that should result in enduring change.