This pilot study tested the feasibility and effectiveness of a social support intervention with women (n = 24) while they were in a domestic violence shelter. Health outcomes were examined pre and postintervention using a randomized control design. The intervention group had greater improvement (p = .013) in psychological distress symptoms and greater improvement in perceived availability of social support (p = .016) than the control group. The intervention group showed less health care utilization (p = .032) than the control group. Social support interventions for women in shelters are effective in improving health outcomes. Further research should be aimed at testing the effectiveness of different types of interventions on health and abuse outcomes in women who experience interpersonal violence.

Aim: The aim of this study was to investigate the effectiveness of blended learning with a flipped classroom design on student academic achievement in a Bachelor of Science in Nursing course.Design: A quasi‐experimental study.Method: Students were split into an experimental blended learning with a flipped classroom design group and a control group using the traditional, teacher‐centred learning method. Data were collected during spring 2018 (13.3 weeks) and student's grades for the registered course and their grade point average (GPA) were recorded.Results: Findings showed statistically significant increases in student grades in the experimental group. Predictability calculations also showed better achievement of learning outcomes if a blended learning with a flipped classroom design is continued to be used in the future.

Intimate partner violence (IPV) is a public health problem. The purpose of this study was to compare the effectiveness of the HELPP (Health, Education on Safety, and Legal Support and Resources in IPV Participant Preferred) intervention among IPV survivors. A sequential, transformative mixed-methods design was used. Participants were randomly assigned to one of three study groups: Online (ONL), Face-to-Face (FTF), and Waitlist Control (WLC). The HELPP intervention was offered to 32 adult female participants who were 45.2% Asian, 32.3% White, and 22.5% Black. Outcome measures were anxiety, depression, anger, personal, and social support. In total, 64% (n = 20) of the participants reported having experienced IPV before the age of 18. The anger mean score pre-test to post-test difference was significant for ONL (p < 0.001) and WLC (p = 0.01). The personal and social support pre-test to post-test mean score differences were significant for ONL (p < 0.001; p < 0.001) and WLC (p = 0.01; p = 0.006), respectively. The HELPP intervention (1) decreased anxiety, depression, anger, and (2) increased personal and social support in the ONL group. The HELPP information and intervention was shown to be feasible, acceptable, and effective among IPV survivors compared with participants in the WLC group. The WLC participants displayed (1) increased levels of anxiety, depression, and anger and (2) decreased levels of personal and social support, post-intervention. Further research could be conducted to determine if e-mail alone or e-mail plus mobile devices are more useful modes of delivering interventions.

Domestic violence (DV) is a public health crisis that threatens both the mental and physical health of people. With the unprecedented surge in data available on the internet and electronic health record systems, leveraging machine learning (ML) to detect obscure changes and predict the likelihood of DV from digital text data is a promising area health science research. However, there is a paucity of research discussing and reviewing ML applications in DV research. Methods: We extracted 3588 articles from four databases. Twenty-two articles met the inclusion criteria. Results: Twelve articles used the supervised ML method, seven articles used the unsupervised ML method, and three articles applied both. Most studies were published in Australia (n = 6) and the United States (n = 4). Data sources included social media, professional notes, national databases, surveys, and newspapers. Random forest (n = 9), support vector machine (n = 8), and naïve Bayes (n = 7) were the top three algorithms, while the most used automatic algorithm for unsupervised ML in DV research was latent Dirichlet allocation (LDA) for topic modeling (n = 2). Eight types of outcomes were identified, while three purposes of ML and challenges were delineated and are discussed. Conclusions: Leveraging the ML method to tackle DV holds unprecedented potential, especially in classification, prediction, and exploration tasks, and particularly when using social media data. However, adoption challenges, data source issues, and lengthy data preparation times are the main bottlenecks in this context. To overcome those challenges, early ML algorithms have been developed and evaluated on DV clinical data.

Effective intervention strategies for abused women must be developed that match women's perceived needs. The purpose of this study was to examine the associations among age, race, employment status, education levels, and women's perceptions of social support. Women ( n = 40) from 19 to 68 years of age in a family court setting or domestic violence shelter completed the Interpersonal Support Evaluation List (ISEL), which was used to measure subscales of companionship, self-esteem, emotional support, and instrumental support. Abuse was documented by patient reports and protocols of the referring agencies. Significant associations were found between age and self-esteem (r =. 47, p =. 002) companionship (r =. 29, p =. 07) and emotional support (r =. 27, p =. 09). Also significant were the associations between race and self-esteem (r =. 27, p =. 10) and employment and instrumental support (r = m. 32, p =. 08). These significant findings may aid in the development of interventions for abused women that are matched to their social support needs, particularly if the findings are consistent in larger studies.