23/08/2026
We proudly congratulate Engr. Angelus Vincent Guilalas, a faculty member of the College of Engineering and Information Technology, for representing SPUS in the 5-Day Training-Workshop on Systematic Review and Data Analysis for Health Research hosted by DOH-Caraga in Butuan City.
Your commitment to continuous learning and research excellence is truly inspiring!
๐๐๐ ๐๐ฎ๐ซ๐ข๐ ๐๐จ ๐๐๐๐ฎ๐ฅ๐ญ๐ฒ ๐ฃ๐จ๐ข๐ง๐ฌ ๐๐๐ ๐๐๐ซ๐๐ ๐ ๐-๐๐๐ฒ ๐ซ๐๐ฌ๐๐๐ซ๐๐ก ๐ญ๐ซ๐๐ข๐ง๐ข๐ง๐ ๐ข๐ง ๐๐ฎ๐ญ๐ฎ๐๐ง
St. Paul University Surigao (SPUS) strengthened its research capacity in health data analytics as Engr. Angelus Vincent Guilalas, a faculty member from the College of Engineering and Information Technology, represented the institution in a five-day intensive training-workshop hosted by the Department of Health (DOH) โ Center for Health Development Caraga.
Held from July 17 to 21, 2026, at the Grand Palace Hotel in Butuan City, the "Training-Workshop on Systematic Review and Data Analysis for Health Research" focused on equipping academic and health researchers with a structured approach to building rigorous systematic review protocols. The program covered foundational concepts in evidence synthesis, where participants explored core methodologies to differentiate scoping, systematic, and meta-analysis review approaches. On the second day, attendees established precise investigation parameters using standardized frameworks like PICO and PCC to define strict inclusion and exclusion criteria.
The middle phase of the workshop focused on methodological blueprints and search operationalization. On the third day, participants built formal review protocols following PRISMA-P and PRISMA 2020 standards while learning protocol registration through platforms such as PROSPERO and OSF. The fourth day transitioned into practical application as attendees developed database search strategies for PubMed and Scopus, utilizing Covidence software to streamline duplicate removal, abstract screening, and conflict resolution.
The intensive training concluded with a focus on data extraction, critical appraisal, and review finalization. Participants examined structured extraction parameters for prediction-model studies using the CHARMS framework, alongside PROBAST+AI and TRIPOD+AI parameters to evaluate risk of bias and methodological quality in artificial intelligence and machine-learning models. Guilalas's participation highlights SPUS's continuous drive to foster interdisciplinary health research and integrate advanced data analytics into engineering and information technology education.