Southern Leyte State University (SLSU) is a premier public university in the Philippines. It is mandated to provide advanced education, higher technological, professional instruction and training in trade, fishery, agriculture, forestry, science, education, commerce, engineering and related courses. It is also mandated to undertake research and extension services, and provided progressive leadership in its areas of specialization. Formerly the Southern Leyte State College of Science and Technology and Tomas Oppus Normal College, SLSU was created through the passage of Republic Act 9261 on March 7, 2004. SLSU is the only State University in the province of Southern Leyte.
This review investigates the intellectual structure, key contributors, and thematic evolution of studies on work relationship and organizational performance based on Scopus-published articles from 1985 to 2025. This review also explores research gaps and future directions under this domain. The study used bibliometric tools such as VOSviewer and Bibliometrix to analyze publication trends, citation patterns, co-authorship networks, and keyword co-occurrences. This paper also used content analysis in determining the research gaps and future directions. Results show an outstanding surge in research production, especially from 2021 to 2024, where interest was most focused on employee engagement, effectiveness in leadership, and team dynamics. However, findings reveal methodological limitations and fragmented collaboration networks, particularly among institutions from underrepresented regions. The analysis identifies emerging themes such as corporate culture, diversity, and perceived organizational support as future research priorities. By mapping the field’s evolution and highlighting underexplored areas, this research offers strategic direction for advancing inclusive, evidence-based practice to improve work relations and organizational performance in various settings. The results inform scholars, practitioners, and policymakers who promote high-performing, resilient, and inclusive organizational cultures.
This study examined the usage and perceptions of AI-based learning tools among Grade 12 STEM learners at Hilongos National Vocational School (HNVS). Specifically, it aimed to identify the respondents’ demographic profile in terms of age and sex, determine the AI-based learning tools commonly used, assess the frequency of AI tool usage, evaluate students’ perceptions in terms of engagement, interaction, behavioral intentions, satisfaction, and perceived improvement in academic performance, and determine whether a significant relationship exists between AI tool usage and students’ perceptions. The study employed a descriptive-correlational quantitative research design. Using stratified random sampling, a total of 76 Grade 12 STEM learners were selected from the STEM population of HNVS during the School Year 2025–2026. Data were gathered through a 20-item standardized questionnaire adapted from Khairuddin, Kamaruddin, and Alwi (2024), with minor modifications to fit the context of the study. Descriptive statistics such as frequency, percentage, mean, and standard deviation were used to summarize the data, while Pearson correlation coefficient was used to determine the relationship between variables. Findings revealed that ChatGPT was the most commonly used AI-based learning tool, and the majority of respondents reported often using AI tools for academic purposes. Students’ overall perceptions of AI-based learning tools were interpreted as neutral, indicating moderate views regarding their influence on engagement, interaction, behavioral intentions, satisfaction, and academic performance. Furthermore, the results showed a moderate positive and statistically significant relationship between AI tool usage and students’ perceptions (r = 0.468, p < 0.001). The study concluded that AI-based learning tools can serve as supportive academic resources when used appropriately and responsibly, but their effectiveness depends on the extent and manner of their integration into students’ learning practices.
This cross‑sectional study assessed the prevalence, predictors, and substance use risk profile among 333 incarcerated males at the Freetown Male Correctional Centre. The WHO-ASSIST tool was used for data collection. The risk of substance-related issues was categorised as low, moderate, or high. Logistic regression was done to identify predictors of lifetime substance use (Model I) and recent use within the past three months within or outside prisons (Model II). The prevalence of lifetime substance use was 88.6% (95% CI: 0.85-0.92), recent use 73.9% (95% CI: 0.69-0.79), and polysubstance use 77.2% (95% CI: 0.73-0.82). Tobacco (80.5%), cannabis (70.0%), alcohol (68.2%), and opioids (64.0%) were the most used substances. In adjusted models, older age, unmarried status, and rural residence were significant predictors of both lifetime and recent use, while education status was specifically associated with recent use. Risk profiles revealed a high level of dependence among tobacco users and moderate risk among most cannabis and opioid users. Substance use was notably high, with tobacco, cannabis, and opioids emerging as the primary substances of concern. These findings underscore the burden of substance use and highlight the need for targeted interventions and the integration of cessation programs within the correctional centre.
Mapping the Philippines’ contribution to global research on technology business incubators (TBIs) remains underexplored. This paper sought to address this gap by conducting a comprehensive bibliometric analysis to depict the country’s position within the regional and global domains. Applying the Zupic and Čater’s framework and the PRISMA guidelines, this paper analyzed 500 Scopus documents published between 1988 and 2025 using Biblioshiny and VOSviewer to determine co-authorship, citations, and thematic trends. Evidence indicates that the Philippines makes a smaller global contribution to TBI research, as reflected in research output (1.8
Social media addiction among students has become a significant concern, often linked to academic decline, disrupted sleep, and poor mental health. This study aimed to predict students' social media addiction stages using a comparative machine learning approach. A dataset containing demographic and behavioral attributes was analyzed to classify addiction into low, moderate, and high stages. Three classifiers LightGBM, J48, and DecisionStump were implemented and evaluated using precision, recall, F1-score, and confusion matrices. LightGBM achieved the highest accuracy (98 %), followed by J48 (97.87 %) and DecisionStump (85.82 %). Visual analyses revealed notable patterns, including higher high-stage predictions among female students and elevated addiction risk among those aged 19-21. Findings demonstrate that integrating machine learning with demographic insights enables accurate detection and visualization of addiction stages. The proposed framework is practical, interpretable, and scalable, offering valuable tools for educators, mental health practitioners, and policymakers for early intervention.
