An Exploratory Study of Healing Circles as a Strategy to Facilitate Resilience in an Undocumented Community

Within the United States, the COVID-19 pandemic highlighted critical inequalities affecting undocumented communities and resulting in particularly heightened stress for members of these communities. In addition to the stress associated with COVID-19, immigrants in the United States were more than ever subjected to a hostile antiimmigrant climate under Trump’s administration. Given this compounded stress, the impact of the pandemic on mental health is likely to be disproportionately experienced by undocumented immigrants. In response, a group of psychologists partnered with a leading immigrant rights advocacy organization and formed a reciprocal collaboration to support undocumented communities. A major focus of the collaboration is to foster learning, supporting members of the immigrant community to contribute to their own well-being and others in the community. Accordingly, the collaborative developed and delivered a web-based mental health education session to the immigrant community and to practitioners serving this population. The session presented the use of healing circles as a strength-based approach to building resilience and also sought feedback regarding specific features of healing circles that can enhance their effectiveness in managing distress. Survey data and qualitative findings from this study show that those who participated in the web-based program perceived the session as validating and informative. Findings also underscored the need for creating safe spaces for community members to be vulnerable about their lived experiences while promoting ownership of their narratives. We discuss practical implications pertaining to the development and facilitation of social support groups for immigrants led by nonspecialist community members trained for this role. Impact Statement We describe a reciprocal collaboration between psychologists and an immigrant-led advocacy organization for the purposes of supporting undocumented immigrants in tailoring culturally congruent therapeutic approaches for fostering resilience as they face multiple stressors due to interlocking crises, such as the COVID-19 pandemic and antiimmigrant policies. The collaboration led to the development and delivery of a web-based session that provided immigrant community members and practitioners with recommendations for facilitating healing circles as a strength-based and culturally responsive approach to fostering peer-led social support during stressful times. Findings highlight the need for creating such safe spaces for community members to be vulnerable about their lived experiences and feel validated

Improved Diagnosis of Breast Implant Rupture with Sonographic Findings and Artificial Neural Networks

Rationale and Objectives. The authors evaluated the use of sonographic findings combined with artificial neural networks as an aid to the diagnosis of breast implant rupture. Materials and Methods. From a database of 78 breast implants that were evaluated prospectively with sonography and then surgically removed, sonographic findings and surgical results were used to train and test backpropagation and radial basis function artificial neural networks by using the leave-one-out method. Receiver operating characteristic (ROC) curve analysis was used to compare the performance of the different neural networks with that of the radiologists involved. Results. By using the ROC area index as a measure of performance, the artificial neural network (Az = 0.8744) outperformed the radiologists (Az = 0.8057), although not by a statistically significant difference (P = .09). The best-performing network used, in addition to the sonographic findings, the diagnosis of the radiologists as an input. This network (Az = 0.9245) outperformed both the radiologists and the “unaided” networks by a statistically significant margin (P = .02 for radiologists, P = .04 for the unaided network). The network performed remarkably well in those cases in which the radiologists classified the implant as indeterminate, predicting the correct diagnosis in 23 of 25 cases (92%). Conclusion. The results suggest that artificial neural networks in tandem with the unaided radiologic diagnosis can improve the accuracy rate in the detection of implant rupture based on sonographic findings. This “team” approach provided the best results

Measuring Satisfaction with Mammography Results Reporting

OBJECTIVE: To assess factors associated with patient satisfaction with communication of mammography results and their understanding and ability to recall these results. DESIGN: Cross-sectional telephone survey. SETTING: Academic breast imaging center. PATIENTS: Two hundred ninety-eight patients who had either a screening or diagnostic mammogram. MEASUREMENTS AND MAIN RESULTS: Survey items assessed waiting time for results, anxiety about results, satisfaction with several components of results reporting, and patients' understanding of results and recommendations. Women undergoing screening exams were more likely to be dissatisfied with the way the results were communicated than those who underwent diagnostic exams and received immediate results (20% vs 11%, P = .05). For these screening patients, waiting for more than two weeks for notification of results, difficulty getting in touch with someone to answer questions, low ratings of how clearly results were explained, and considerable or extreme anxiety about the results were all independently associated with dissatisfaction with the way the results were reported, while age and actual exam result were not. CONCLUSIONS: Patients undergoing screening mammograms were more likely to be dissatisfied with the way the results were communicated than were those who underwent diagnostic mammograms. Interventions to reduce the wait time for results, reduce patients' anxiety, and improve the clarity with which the results and recommendations are given may help improve overall satisfaction with mammography result reporting