Research has shown that sibling sexual harm (SSH) can result in psychological difficulties during childhood and adulthood. Yet the literature remains mixed regarding whether these outcomes may be different from child sexual abuse (CSA) perpetrated by other family members or those outside of the family unit. This study aimed to examine mental health functioning of adult survivors who experienced SSH as compared to non-sibling intrafamilial and extrafamilial sexual abuse. Participants include 532 adult survivors of CSA who reside in the United States. Participants completed an anonymous, online self-report survey. Overall, we found that individuals who experienced SSH by a biological sibling experienced mental health issues including posttraumatic stress, depression, suicidal ideation, guilt, and shame. These symptoms were largely experienced at similar levels to those who experienced non-sibling intrafamilial and extrafamilial sexual abuse. Those who experience SSH reported greater distress in comparison to those who experienced extrafamilial sexual abuse. When combining SSH and other forms of intrafamilial abuse, those who experienced intrafamilial CSA had lower rates of guilt-related Hindsight Bias/Responsibility ratings compared to those who experienced extrafamilial CSA. However, there were no other significant differences in mental health issues reported by adults who experienced intrafamilial versus extrafamilial CSA. The findings have important implications for both the detection and prevention of SSH.
Argument visualization is widely taken up in higher education as a way to help students reason and write more effectively. We seek to examine the overall effect of argument visualization on student achievement, the moderating role of learner characteristics and instructional characteristics, and the teaching implications for higher education. Following PRISMA guidelines, we synthesized 14 studies (k = 20 effect sizes, N = 1455) using random-effects models, Hedges’ g, moderator analyses, and publication bias assessments (Egger’s test, trim-and-fill). The unadjusted overall effect was large (g = 1.594, 95
Background: Despite continuous glucose monitoring (CGM) being the standard of treatment for type 1 and type 2 diabetes, patient uptake has been reported to under 50%. Clinician awareness and identification of barriers can help reduce diabetes-related complications; however, less than half of pharmacy schools provide CGM education. The objective is to explore pharmacy students' perceptions of a CGM wear experience and their self-reported awareness of patient challenges, device usability, and empathyrelated considerations. Methods: A CGM student-wear experience was incorporated into two sessions of an elective advanced pharmacotherapy course for third-year pharmacy students. The experience was divided into three parts over two class sessions, including a one-week CGM student-wear experience. Reflections were collected through an anonymous questionnaire and a recorded focus group. A thematic approach guided analysis, and reviewers reached consensus on themes. Results: Seventeen students participated; 70.6% had prior experience with traditional finger-stick blood glucose monitoring, while only one had CGM experience. Students described four areas of awareness: empathy for patient experiences, recognition of CGM as a self-management tool, perceived value of hands-on learning, and anticipated barriers. Conclusion: Students perceived greater awareness of CGM-related challenges and patient experiences, suggesting that application-based activities can support patient-centred learning in pharmacy curricula.
While Unified Multimodal Models (UMMs) have achieved remarkable success in cross-modal comprehension, a significant gap persists in their ability to leverage such internal knowledge for high-quality generation. We formalize this discrepancy as Conduction Aphasia, a phenomenon where models accurately interpret multimodal inputs but struggle to translate that understanding into faithful and controllable synthesis. To address this, we propose UniCorn, a simple yet elegant self-improvement framework that eliminates the need for external data or teacher supervision. By partitioning a single UMM into three collaborative roles: Proposer, Solver, and Judge, UniCorn generates high-quality interactions via self-play and employs cognitive pattern reconstruction to distill latent understanding into explicit generative signals. To validate the restoration of multimodal coherence, we introduce UniCycle, a cycle-consistency benchmark based on a Text to Image to Text reconstruction loop. Extensive experiments demonstrate that UniCorn achieves comprehensive and substantial improvements over the base model across six general image generation benchmarks. Notably, it achieves SOTA performance on TIIF(73.8), DPG(86.8), CompBench(88.5), and UniCycle while further delivering substantial gains of +5.0 on WISE and +6.5 on OneIG. These results highlight that our method significantly enhances T2I generation while maintaining robust comprehension, demonstrating the scalability of fully self-supervised refinement for unified multimodal intelligence.
In real-world video question answering scenarios, videos often provide only localized visual cues, while verifiable answers are distributed across the open web; models therefore need to jointly perform cross-frame clue extraction, iterative retrieval, and multi-hop reasoning-based verification. To bridge this gap, we construct the first video deep research benchmark, VideoDR. VideoDR centers on video-conditioned open-domain video question answering, requiring cross-frame visual anchor extraction, interactive web retrieval, and multi-hop reasoning over joint video-web evidence; through rigorous human annotation and quality control, we obtain high-quality video deep research samples spanning six semantic domains. We evaluate multiple closed-source and open-source multimodal large language models under both the Workflow and Agentic paradigms, and the results show that Agentic is not consistently superior to Workflow: its gains depend on a model's ability to maintain the initial video anchors over long retrieval chains. Further analysis indicates that goal drift and long-horizon consistency are the core bottlenecks. In sum, VideoDR provides a systematic benchmark for studying video agents in open-web settings and reveals the key challenges for next-generation video deep research agents.
