Pregnant Hispanic women are at increased risk for posttraumatic stress disorder (PTSD) in part due to greater risk of childhood maltreatment, intimate partner violence (IPV), and pregnancy-related vulnerabilities. However, PTSD, is a highly heterogenous diagnosis with numerous presentations. Individual PTSD symptoms may be differentially associated with specific types of maltreatment, IPV. Determining how IPV exposure across the lifespan is associated with specific symptoms of PTSD in pregnant Hispanic women is necessary to develop group-relevant models of this disorder and targeted interventions. The present study examined a network model of PTSD symptoms, childhood maltreatment, and adulthood IPV in a sample of pregnant Hispanic women ( N = 198). Childhood emotional abuse and adulthood psychological distress had the highest bridge centrality. These types of exposures were most strongly associated with social isolation. Childhood emotional abuse was associated with more individual PTSD symptoms than any IPV type. These findings suggest that associations between PTSD symptoms and different types of IPV exposure vary. In addition, robust associations between childhood emotional abuse and PTSD symptoms suggest that this domain may be particularly important for the clinical assessment and intervention for pregnant women.

Crowdsourcing platforms have grown in popularity in the last decade. This approach has become an essential data collection method, especially when access to participant pools are limited by location and resources. Concerns about the validity and quality of crowdsourced data persist, however. A recent documented increase in the amount of invalid responses within crowdsourced data has highlighted the need for quality control measures. The present study evaluated the utility of a Cyborg Method that involved two components – an automated evaluation of participant meta-data and a human review of open-ended responses. Two samples were recruited – the first applied the Cyborg Method after data collection to gauge the extent that invalid responses would be collected without such measures. The second applied the method during data collection to determine the method’s ability to proactively screen invalid responses and reduce study costs such that invalid responses were not given compensation. Results suggested that Cyborg Method identified a substantial portion of invalid responses. The use of both automated and human evaluation components was necessary to screen out these responses. Furthermore, the application of the Cyborg Method during data collection substantially reduced the per participant cost of data collection. These results suggest that the Cyborg Method is a promising means by which to maintain the quality of crowdsourced data.