Background: This study on US adult cancer risk perceptions aims to explore the relationship between an individual’s perceived risk of developing cancer and the extent to which they engage in preventative behaviors, such as nutrition and physical activity. Methods: Data were obtained from the 2025 Health Information National Trends Survey 7 (HINTS 7). Descriptive statistics were generated to explore participants’ sociodemographic characteristics and cancer risk perceptions. Multinomial regression analysis was used to examine associations between cancer beliefs and consumption of alcohol, fruit, and vegetables. Results: Using multinomial logistic regression, significant associations (p < 0.001) were reported across all cancer beliefs and daily intake of 1–5 drinks per day, as well as ≥6 drinks per day. Significant associations (p < 0.001) were also reported across all cancer beliefs and daily intake of fruits and vegetables, which was further broken down into <½ cup per day to 1 cup, 1 to <2 cups, 2 to <4 cups, and greater than 4 cups. Conclusions: Our findings support the need for greater cancer knowledge dissemination and awareness in communities.

Disabled people commonly report experiencing predetermined perceptions and assumptions of disability that form barriers towards understanding and treating disabled people equally. This process is known as ableism; a specific type of ableism called benevolent ableism intends to help and solicit assistance towards disabled people, but instead dismisses their capacity to advocate for their own needs, resulting in help that is deemed overly enforced, assumptive, and unwanted. A focus group and one informal interview were conducted to investigate the views and experiences of people living with cerebral palsy (CP), highlighting how benevolent ableism operates in their own lives, with additional forms of ableism also explored. Thematic analysis (TA) was applied to the data, resulting in three broad themes: benevolent ableism, strategies to improve and reduce ableism, and othering. The findings revealed that benevolent ableism was present when help was assumed, overly requested despite rejections of help, and belittling in nature; positive forms of assistance allowed the person with CP to instigate help and was accommodating; other forms of ableism were encountered, such as invasive staring, being treated like a child, charitable praise, being ignored within the medical setting, and lowered perceptions of capabilities. In order to diminish these difficult experiences and encourage practices, behaviours, and ideologies that promote less prejudice, participants mentioned education and improved interaction as being important aspects. Furthermore, this research poses wider implications for family members, teachers, medical professionals, and the general public to learn appropriate approaches toward disabled people and gain a better understanding of disability.

The integration of Artificial Intelligence (AI) into early childhood education presents new opportunities and challenges in fostering cognitive, social, and emotional development. This theoretical discussion synthesizes recent research on AI’s role in personalized learning, educational robotics, gamified learning, and social-emotional development. The study explores theoretical frameworks such as Vygotsky’s Sociocultural Theory, Distributed Cognition, and the Five Big Ideas Framework to understand AI’s impact on young learners. AI-powered personalized learning platforms enhance engagement and adaptability, while robotics and gamification foster problem-solving and collaboration. Additionally, AI tools support children with disabilities, promoting inclusivity and accessibility. However, ethical concerns related to privacy, bias, and teacher preparedness pose challenges to effective AI integration. Furthermore, the long-term effects of AI on children’s social skills and emotional intelligence require further investigation. This theoretical discussion emphasizes the need for interdisciplinary collaboration to develop AI-driven educational strategies that prioritize developmental appropriateness, equity, and ethical considerations. The findings highlight AI’s potential as a transformative educational tool, provided it is implemented thoughtfully and responsibly. The paper aims to address the following research question: How can artificial intelligence (AI) be meaningfully and ethically integrated into early childhood education to enhance learning, while preserving developmental and relational values?

South Africa is facing serious challenges in addressing the needs of women survivors of human trafficking. Trafficked women often experience health issues, physical, sexual, psychological, economic, and social abuse. Women trafficking is a multidisciplinary problem that needs the involvement of a wide range of actors to intervene and render holistic services. Based on a qualitative study conducted at five residential facilities for women in South Africa, this article highlights best practices in rendering services to women survivors of human trafficking. Data was collected from social service providers using one-on-one interviews and analysed using thematic analysis. The findings indicate that the best practices for rendering effective services to women survivors of human trafficking include rendering services that have an emphasis on emotional containment, prevalence of basic counselling, active referral for medical and other services, and ensuring accurate assessment of the needs and concerns of women survivors of human trafficking. Furthermore, the best practices include establishing a one-stop, all-inclusive service centre; engaging women in skills development and empowerment activities; and ensuring collaboration between all stakeholders involved in rendering services to women survivors of human trafficking. The conclusion is that best practices in rendering effective services to women survivors of human trafficking require an interplay of systems and the presence of sufficient institutional and infrastructural arrangements.

Background: Little is known about women’s contraceptive use in the United States during the novel coronavirus (“COVID-19”) pandemic and the risk of unintended pregnancy. Methods: We compared the weighted response rates on contraception use for female respondents aged 18–44 from the Behavioral Risk Factor Surveillance System (BRFSS) between 2019 and 2022. Results: Our study reveals a significant increase of 16.1% (CI = 0.145, 0.177) in the proportion of women using contraception in 2022 as compared to 2019. The largest increase in the use of non-reversible contraception was seen in the proportion of female sterilization, at 3.0% (CI = 0.017, 0.043), mostly attributed to non-Hispanic Black individuals with a 12% increase (CI = 0.046, 0.198). The largest decrease was seen in the use of condoms, at 7.4% (CI = −0.094, −0.055). This was driven by both non-Hispanic Black and multiracial groups, each experiencing a 19% decrease (CI = −0.251, −0.127; CI = −0.304, −0.068, respectively). The proportion of women at risk of unintended pregnancy increased by 3.7% (CI = 0.010, 0.063). These increases were observed among those with an income of less than USD 15k, showing a 14.9% increase (CI = 0.037, 0.262). Older females and those with Medicaid insurance were more likely to use female sterilization. Hispanics, college graduates, and those with Medicaid insurance were more likely to use condoms. Non-White females and those without annual checkups were more at risk of unintended pregnancy. Conclusions: Contraceptive methods shifted among females with slightly increased sterilization in the years 2019 to 2022.

Criminal laws and their deserts-based punishments, particularly in Anglo-American systems, remain grounded in folk psychology assumptions about free will, willpower, and agency. Yet advances in neuropsychiatry, neuromicrobiology, behavioral genetics, multi-omics, and exposome sciences, are revealing how here-and-now decisions are profoundly shaped by antecedent factors. This transdisciplinary evidence increasingly undermines the folk psychology model: some argue it leaves “not a single crack of daylight to shoehorn in free will”, while others suggest the evidence at least reveals far greater constraints on agency than currently acknowledged. Historically, courts and corrections have marginalized brain and behavior sciences, often invoking prescientific notions of monsters and wickedness to explain harmful behavior—encouraging anti-science sentiment and protecting normative assumptions. Earlier disciplinary silos, such as isolated neuroscience or single-gene claims, did little to challenge the system. But today’s integrated sciences—from microbiology and toxicology to nutrition and traumatology, powered by omics and machine learning—pose a threat to the folk psychology fulcrum. Resistance to change is well known in criminal justice, but the accelerating pace of biopsychosocial science makes it unlikely that traditional assumptions will endure. In response to modern science, emergent concepts of reform have been presented. Here, we review the public health quarantine model, an emergent concept that aligns criminal justice with public health principles. The model recognizes human behavior as emergent from complex biological, social, and environmental determinants. It turns away from retribution, while seeking accountability in a way that supports healing and prevention.

This article examines patterns of enrollment, academic performance, and dropout among women in engineering programs at the State University of Milagro (UNEMI), Ecuador, between 2016 and 2023. Using a quantitative approach that combines descriptive statistics with exploratory factor analysis, the study identifies critical obstacles affecting the academic persistence of female students. Results show a 291% increase in female enrollment following the implementation of institutional policies focused on inclusion and the expansion of virtual education, reflecting the cumulative growth rate in female enrollment from 296 students in 2016 to 1158 in 2023. However, this growth coincided with a decline in pass rates and an increase in dropout levels. Among the main factors influencing student attrition are financial limitations associated with transportation, access to technology, and study materials. Additional elements include motivational aspects and insufficient academic preparation. The findings highlight the importance of strengthening institutional strategies that promote academic equity, create inclusive learning environments, and respond effectively to the personal and structural challenges that women face in engineering education. This study contributes empirical evidence that can inform university policies and public initiatives aimed at fostering greater participation and success for women in science and technology fields.

This systematic review synthesizes a decade of peer-reviewed research (2015–2025) examining the interplay of filter bubbles, echo chambers, and algorithmic bias in shaping youth engagement within social media. A total of 30 studies were analyzed, using the PRISMA 2020 framework, encompassing computational audits, simulation modeling, surveys, ethnographic accounts, and mixed-methods designs across diverse platforms, including Facebook, YouTube, Twitter/X, Instagram, TikTok, and Weibo. Results reveal three consistent patterns: (i) algorithmic systems structurally amplify ideological homogeneity, reinforcing selective exposure and limiting viewpoint diversity; (ii) youth demonstrate partial awareness and adaptive strategies to navigate algorithmic feeds, though their agency is constrained by opaque recommender systems and uneven digital literacy; and (iii) echo chambers not only foster ideological polarization but also serve as spaces for identity reinforcement and cultural belonging. Despite these insights, the evidence base suffers from geographic bias toward Western contexts, limited longitudinal research, methodological fragmentation, and conceptual ambiguity in key definitions. This review highlights the need for integrative, cross-cultural, and youth-centered approaches that bridge empirical evidence with lived experiences.

In the digital age, generative artificial intelligence (GenAI) development has brought about structural transformations in higher education. This study examines how students’ regular use of artificial intelligence tools brings a new active player into the educational process. This is an “uninvited assistant” that changes traditional models of teaching and learning. This study was conducted using the following standard methods: bibliometric analysis, student survey using an electronic questionnaire, primary processing and graphical visualization of empirical data, calculation of statistical indicators, t-statistics, and z-statistics. As the results of the bibliometric analysis show, the evolution in the perception and integration of artificial intelligence within higher education discussions, as evidenced by the comparison of network visualizations from 2020 to the present, reveals a significant transformation. Based on a quantitative survey of 1197 undergraduate students in five Eastern European countries, this paper proposes a conceptual shift from the classic two-dimensional (2D) model of higher education services based on university teacher–student interactions to a three-dimensional (3D) model that includes artificial intelligence as a functional third player (an uninvited assistant). Statistical hypothesis testing confirms that students need AI and regularly use it in the learning process, facilitating the emergence of this new player. Based on empirical data, this study presents a hypothetical 3D model (X:Y:Z), where the Z-axis reflects the intensity of AI use. This model challenges traditional didactic frameworks and calls for updating educational policies, ethical standards, and higher education governance systems. By merging digital technologies and social change, the results provide a theoretical and practical basis for rethinking pedagogical relationships and institutional roles in the digital age.

This paper explores the methodological implications, processes, and opportunities relating to the use of participatory approaches in the study of intersecting inequalities. Within the context of an international, interdisciplinary project (Partnerships for Equality and Inclusion (PEI)), four sub-projects elected to use participatory methodologies to engage with marginalized and excluded communities so that their needs, voices, and knowledge were included in the data collection process and the dissemination of findings at micro (community) and macro (policy) levels. The four project teams in Kenya, Vietnam, Myanmar, and Nigeria engaged with training and research processes so that participants with lived experience could contribute both as peer researchers and in focus groups that contributed study data. In this paper, we bring together the findings and learning from each project about how these methods contributed to equity in the research process. We conclude with insights and recommendations on how participatory methods can enable an intersectional and grounded perspective from people facing multiple inequalities in periods of crisis and political change.