Intimate partner violence and delays in obtaining contraception among young people in California and Texas.

University–community engagement and sustainability change: Opportunities and constraints for business schools

Impact of metal bioaccumulation on cytogenotoxic, haemato-biochemical and histopathological parameters of toads and lizards collected from a North-central University community, Nigeria

ABSTRACT The mental well-being of university students is of significant concern globally, prompting the development of safeguarding protocols in various countries. In this regard, identifying protective factors within the university community is crucial. To address this issue, the current cross-sectional study investigated the relationships between emotional intelligence, emotional regulation, affectivity, and anxiety in 1,337 Peruvian university students aged 18–30, exploring gender differences. A general model is proposed to assess the beneficial effects of emotional intelligence and emotional regulation on affectivity and anxiety. The results showed that (a) emotional intelligence is related to emotional regulation (cognitive reappraisal and expressive suppression), and both are related to affectivity and anxiety; (b) Peruvian females show lower levels of positive affect than males, which, coupled with difficulties in emotional regulation, implies a significantly greater risk for developing anxious symptomatology; and (c) structural equation modelling revealed that Peruvian undergraduate students with perceived emotional intelligence tend to utilise cognitive reappraisal more and expressive suppression less, leading to increased positive affect, decreased negative affect, and lower anxiety levels. This study has important theoretical and practical implications, highlighting the most effective emotional strategies to reduce anxiety and promote positive mental health among university students, while also considering gender differences.

Introduction Prediabetes is a highly prevalent metabolic condition that significantly increases the risk of developing type 2 diabetes and cardiovascular disease. Despite its clinical importance, over 80% of individuals with prediabetes remain undiagnosed. Voice analysis has emerged as a non-invasive, accessible method for disease screening, with prior work showing promising results in detecting hypertension and type 2 diabetes from acoustic features. This study investigates whether voice-based machine learning models can identify individuals with prediabetes and evaluates the generalizability of these models across populations. Methods Participants were recruited from clinical sites in India and a community college in Canada. All participants recorded the same spoken phrase multiple times daily via a mobile app, and glycemic status was assessed using HbA1c levels. Voice recordings were preprocessed to remove silence and trimmed to exclude potentially uninformative sections. A total of 167 acoustic features were extracted from each sample using Librosa, scipy, and parselmouth. Features were averaged per participant. Sex-specific models were developed under six experimental configurations varying by dataset balance (age/BMI-matched vs. unbalanced) and BMI inclusion. Feature selection was conducted using L1-regularized logistic regression (LASSO), and SMOTE was applied during training to address class imbalance. Twelve machine learning classifiers were evaluated using leave-one-subject-out cross-validation (LOSO-CV) on the India dataset. Final models were tested on a holdout India subset and the independent Canada dataset. Results In cross-validation, the best female model (XGBoost, balanced, no BMI) achieved a balanced accuracy of 0.78, and the best male model (Random Forest, balanced, no BMI) achieved 0.68. However, holdout set testing identified different optimal configurations for generalization: the male XGBoost model trained on an unbalanced dataset outperformed the cross-validated model. In the Canada dataset, models failed to generalize effectively, with several configurations unable to correctly identify prediabetic participants. Discussion Voice-based prediction models show potential for prediabetes screening in controlled populations, but their performance declines when applied across geographic or demographic boundaries. These findings highlight the need for more diverse training data and population-specific model tuning to support real-world applicability.

Through this study, we seek to quantify and describe the available lexicon used by students in the technical cycle at IUB in their mathematical communication and its impact on the teaching and learning process, by means of a database collected from IUB students in the 2025-1 and 2025-2 academic terms. Through this database, teachers and the university community in general will be able to determine more precisely the scientific contribution they can make to each individual who begins their university studies; furthermore, it will be possible to determine the extent to which students’ lexical availability influences the teaching–learning process at the beginning of their university studies, after the completion of basic secondary education. Following the collection and statistical analysis of the data, the aim is to propose curricular adjustments at the levels of primary, secondary, and university education, as well as to generate more effective strategies in the teaching and learning processes of mathematics and related areas within the IUB community.

TVET 4.0: Integrating Digital Pedagogy and Industry 4.0 Skills into Malaysian Community College Programmes

This study examines the practices of U.S. community (two-year) colleges earning the Carnegie Elective Classification for Community Engagement. Community colleges are not widely represented in the Elective Classification. In keeping with each state’s charge of workforce and economic development and training, community colleges focus on teaching as compared to four-year universities conducting research. Using thematic analysis, the study found four themes across the 26 colleges earning the prestigious designation from 2006-2024. The study documents the practices of institutionalized service-learning and community engagement at community colleges achieving the Carnegie Elective Classification for Community Engagement and interprets the findings through a conceptual framework of Institutional Logics focusing on Bureaucratic/State and Market Logics as key obstacles to expanded participation by the colleges. A much-needed future research agenda on service-learning and community engagement within the two-year college sector is proposed framed in Institutional Logics.

ABSTRACT This article celebrates the accomplishments of the Community College Journal of Research and Practice (CCJRP) throughout 2025 – a year that concludes as the journal reaches a significant milestone and enters its 50th year of publication. Over the past five decades, the CCJRP has served as a leading platform for advancing research and practice in the community college sector, marking milestones of scholarly growth while building momentum that continues to shape the field. This article highlights the impactful studies, featured articles, and special issues that have strengthened the journal’s reputation for both scholarly rigor and practitioner relevance. It also acknowledges the essential contributions of our editorial board members, authors, and editors, whose commitment to inquiry and innovation sustains the journal’s enduring influence. Looking ahead, this article previews issues planned for 2026, including a special issue honoring the legacy of a distinguished community college scholar–practitioner, as well as forthcoming issues focused on workforce education, student pathways and success, social media and technology, COVID-19 impacts, STEM programs, and other emerging topics vital to community colleges and higher education. Together, these efforts commemorate 50 years of publishing excellence while reaffirming the CCJRP’s mission to elevate research, inform practice, and shape the future of community college education – carrying the momentum of past milestones forward into the decades to come.

The COVID-19 pandemic exposed deep structural and psychological challenges in higher education, demanding leadership approaches that balance empathy with institutional resilience. This study explores how trauma-informed leadership mitigates resistance to change in Vietnam’s post-pandemic universities. Using qualitative data from three institutions, it examines how principles of safety, trust, empowerment, and cultural sensitivity shaped adaptation processes. Findings show that trauma-informed leadership reduced resistance by fostering psychological safety, transparent communication, and shared ownership of change. Leaders localized trauma-informed practices to align with Vietnamese collectivist and hierarchical values, reframing empathy and care as collective responsibilities. Despite resource limitations, these approaches strengthened engagement and resilience across university communities. The study extends the global literature by demonstrating trauma-informed leadership’s adaptability beyond Western contexts, highlighting its reliance on contextual sensitivity, emotional intelligence, and consistent communication to transform crisis recovery into sustainable organizational learning.

Accurately predicting Li-ion battery capacity trajectories using early-life data can dramatically improve battery-life understandings and be used to rapidly evaluate design/cost/performance trade-offs when developing new battery materials. Accurate early-life predictions enable researchers to quickly iterate over cell designs and material precursor properties without consistently cycling cells to failure. To this end, we present a toolbox that uses a combined Gaussian process and Bayesian regression approach that capitalizes on signals other than just capacity (e.g., dQ/dV, voltage difference between charge and discharge) to predict capacity-fade trajectories.The prediction tool uses Bayesian regression to fit functional forms, e.g., power law, sigmoids, etc., to predict capacity-fade dynamics. By fitting functional forms, the capacity fade can be interrogated at any point in the future, allowing for early cell-failure prediction. Additionally, Bayesian regression allows for accurate uncertainty estimates that account for cell-to-cell variability (aleatoric uncertainty) and the lack of observation data (epistemic uncertainty). By only using early cycle data to predict the capacity fade trajectory, uncertainty bounds at end-of-life can be extremely large (see Fig. 1a). The large uncertainty bounds are further exacerbated because there is no systematic way to define the prior distribution of the functional forms’ parameters.We improve the predicted trajectory confidence interval using two methods. First, we show that a small amount of held-out cycling data is sufficient to derive appropriate prior distributions for the functional forms’ parameters, effectively leading to data-driven priors. We propose constructing the data-driven priors by first running Bayesian regression starting with uninformed priors to generate intermediate cell-specific posterior parameter distributions. These posterior distributions are combined using a Gaussian mixture model for each parameter to create the data-driven priors. Second, we use a wide variety of electrochemical features extracted from the first few weeks of testing, such as changes in cell thermodynamics (log(|mean(ΔdQ/dVw3 - w0 (V)|)) or cell kinetics (Δ(ΔV(50%SOC)) w3-w0 ), to train Gaussian process models that predict the capacity in the near-term future, and then using these predictions with their associated uncertainty in addition to the measured early cycle data to estimate the degradation trajectory. Notably, these two methods (data-driven priors and prediction-informed trajectories) are complementary and can be combined.We evaluate the performance of our proposed method on an open-source dataset from Iowa State University and Iowa Lakes Community College (ISU-ILCC)1. Our initial findings suggest that, when only few observations are available, a power law functional provides the most accurate predictions. However, a twin sigmoidal function becomes more accurate as the number of observations further increases. We also find that using as little as 10% of the dataset towards generating data-driven priors can lead to significant improvement in prediction accuracy when using early cycle data. Last, we found that augmenting early-cycle data with Gaussian process-predicted capacity data greatly improves the prediction accuracy. We will present a comprehensive comparison of our methods to other methods available in the literature2 and apply this method to additional battery datasets.Figure 1. (a) Predicted capacity fade trajectory for a particular cell without data-informed priors and without Gaussian process-predicted mid-life responses. (b) Predicted capacity fade trajectory incorporating data-informed priors and Gaussian process-predicted mid-life responses. The 3 red dots indicate measured responses, the black dots indicate future (unseen) responses, the black line indicates the averaged predicted trajectory using Bayesian regression, the blue shaded area indicates the prediction envelope, and the 14 green dots with error bars indicate the mid-life Gaussian process-predicted responses.

The purpose of universities in the 21st century is not only to provide quality education and research but also to activate knowledge, teaching and practice for the public good. Models of educational institutions, built on reciprocal knowledge transfer, give way to new models that focus on transformational partnerships with social institutions and communities. This article presents an overview of the practices of leading global universities in the field of promoting psychological wellbeing. The analysis is based on publicly available data from 102 universities included in the University Impact Rankings 2024. In 97 of them, 282 practices aimed at supporting psychological well-being were identified and described. These practices cover a wide range of initiatives from clinical services to educational and social projects, and are deeply integrated into the educational and research activities of universities. The article presents the results of quantitative and qualitative analysis of four groups of practices – psychological assistance and support, information and educational projects, social initiatives and other types of well-being support, according to 127 indicators, structured by groups of organizational conditions, content and positioning of practices inside/outside the university, including invariant types (level, duration, goals, topics, attraction mechanism, awards). The results show that universities systematically contribute to improving the quality of life and solving socially significant problems of populations and university communities. When comparing foreign practices with domestic experience, differences were identified due to the commercial nature of foreign education, local rootedness of practices, greater development and diversity of practices of direct psychological support.

Solving the problems of modern Russian higher education requires the search for new methodological approaches. Such an approach can be a generational approach. One of the key issues of applying this approach in the sociology of higher education, which has not been adequately reflected in scientific literature, is the search and justification of criteria and generational boundaries of the university community. The scientific novelty of the research consists in an attempt to study the ideas of the employees of modern Russian universities about the criteria and boundaries of generations of university communities. For the first time, a qualitative empirical study (interview) of university employees in the Ural Federal District (n=41) was conducted. It allowed us to record a wide range of quantitative (age, work experience) and qualitative (the education system in which the employee was formed; the historical period of the employee’s inclusion in the higher education system; position as a stage of mastering the content of work; attitude to digital technologies and the degree of mastery of them; initiative / knowledge and experience; flexibility and adaptability to changing external conditions, etc.) criteria for differentiating the university community. The mechanisms of self-identification of university employees with a specific generation have been identified. It has been established that the foundation that gives stability to the university is the middle generation. The article contributes to the ongoing discussion about the generational structure of the university community by offering empirical evidence substantiating the relevance and prospects of using the generational approach in higher education. The results of the study make it possible to expand the theoretical understanding of the processes that take place in Russian higher education and to develop practical recommendations for the development of a meaningful and differentiated age policy at universities.

ABSTRACT This study applied Random Forest machine learning to identify predictors of success in gateway English and algebra courses across Wyoming community colleges. High school GPA (HSGPA) consistently emerged as the strongest predictor, while ACT scores added modest incremental value. Gender was relevant in English, with male students at higher risk of underperformance, whereas race and non-traditional status showed limited predictive weight. Nonlinear effects for years since high school suggest that time away from school may influence certain learners. Findings support streamlined placement policies anchored in HSGPA, supplemented by ACT scores when appropriate, and targeted supports for male students in English. This multi-institutional analysis demonstrates how machine learning can clarify predictive patterns in rural community college contexts, informing equitable, data-driven placement strategies that balance simplicity, accuracy, and flexibility for diverse learners.

Today's education must address the challenges posed by an increasingly linguistically diverse society, in which communication has been profoundly reshaped by advances in information and communication technologies. In response to this complex communicative landscape, a qualitative action research study was conducted with a group of undergraduate students enrolled in an intermediate English course at a public university in Colombia. The study aimed to examine to what extent students maximize meaning making in English through the creation of multimodal texts within their university community. During the didactic intervention, which incorporated community pedagogy, multimodal activities were interwoven to scaffold students' multimodal text design. Data were collected through a teacher’s journal, interviews and students’ multimodal eBooks. The findings revealed that multimodality was a powerful means to make community members visible, highlight social issues, and inspire social transformation. Finally, the combination of community pedagogies and multimodality effectively promoted meaning-making in the target language, enhancing students' linguistic competence and critical literacy practices.

The objective of this paper was to investigate the travelling patterns of staff and students of Tertiary Institutions in Ota, Ogun State, Nigeria, using appropriate standard methods including structured questionnaires. The results indicate notable differences in travel patterns between the two groups. Specifically, 74% of students and 77% of staff reported that their commute times are under 30 minutes, suggesting a preference for living close to the universities. The study found that students made 995 trips on Mondays, while staff made 259, highlighting a more active commuting trend among students. Various factors that influence commuting decisions include demographic aspects like age, gender. For example, younger students are more likely to walk, whereas older staff members, often with different responsibilities, tend to use personal vehicles. The research also examines how commuting duration affects academic performance and job satisfaction. Longer commutes, particularly for the 13% of students and 10% of staff who travel for over an hour, are linked to heightened stress and lower satisfaction in their roles. This connection emphasizes the need to tackle commuting challenges to improve educational outcomes and workplace productivity. Additionally, the study points out the necessity for better transportation infrastructure and services to meet the needs of the university community. Recommendations include enhancing public transport, improving pedestrian pathways, and offering flexible work arrangements for staff. By addressing these concerns, the universities can create a more supportive environment that fosters well-being and academic achievement for both students and staff. The findings provide valuable insights for urban planning and transportation management, especially concerning Nigerian universities.

This article traces my intellectual and professional transformation from community college professor to professor-activist that began with my doctoral studies. Divided into four sections, it begins with an overview of the community college mission and the essential role of faculty in advancing the academic and social success of a diverse student population. The second section explores my intellectual development within the University of South Carolina’s EdD program, highlighting how coursework on equity, social justice, and action research deepened my understanding of positionality and reflective inquiry. The third section, becoming, articulates my rationale for an action research dissertation addressing community college faculty burnout and situates my scholarly growth within my evolving activist identity. The final section, beyond, envisions how I will continue leveraging my positionality as a tenured professor to advocate for policies and practices that uplift contingent and early-career colleagues, fostering a more equitable and sustainable academic community.

Retention of Vocational Rehabilitation Customers in Community College

Retention of Vocational Rehabilitation Customers in Community College

This study addresses the critical issue of gender-based violence (GBV) among female students at the Zimbabwe Ezekiel Guti University, Zimbabwe (ZEGU), focusing on the influence of social media. The absence of a comprehensive framework to mitigate GBV in university settings has left many female students vulnerable to online harassment and abuse. This research aims to develop a structured framework that utilises social media as a tool for awareness, support and prevention of GBV. A mixed-methods approach is be employed, combining qualitative and quantitative research methodologies. The sample consists of 200 female students selected through stratified random sampling to ensure diversity in representation. Data collection for the study involves surveys, focus group discussions and interviews to capture a wide range of experiences and perceptions related to GBV and social media usage. The findings inform the creation of a tailored intervention framework, fostering safer online environments and empowering female students. This frame work aims not only to reduce incidents of GBV, but also to promote a culture of respect and safety within the university community.