Salud, Ciencia y Tecnología (Journal)
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Income Paradox and Socioeconomic Determinants of Household Dietary Diversity in Rural Developing Contexts
Full text linkDietary diversity is a crucial indicator of nutritional adequacy and food security, especially in rural developing areas where access to varied foods remains limited. This study aimed to measure household dietary diversity and identify the socioeconomic and behavioral determinants that influence the Food Consumption Pattern Score (Pola Pangan Harapan/PPH) in Nagari Tandikek Utara, Padang Pariaman Regency, Indonesia. A cross-sectional quantitative design was employed, involving 150 households selected through proportional random sampling, with data collected using structured questionnaires, interviews, and field observations. Dietary diversity was assessed across nine food groups using the PPH method, while multiple linear regression analysis (SPSS 23) examined the effects of income, family size, eating habits, mother’s education, head of household’s education, and mother’s age. Results showed that the average PPH score was 51.69%, indicating low dietary diversity, with consumption predominantly centered on cereals (18.26%) and insufficient intake of legumes, animal-source foods, vegetables, and fruits. Regression findings confirmed an income paradox, as income had a significant negative effect (B = −9.398; p = 0.000), while family size (B = −2.294; p = 0.000) and mother’s education (B = −1.077; p = 0.000) also reduced dietary diversity; conversely, the household head’s education improved it (B = 1.070; p = 0.000). These findings indicate that dietary diversity is shaped by intersecting socioeconomic and demographic factors, highlighting the need for integrated strategies—combining nutrition education, gender-responsive approaches, and improved food access—to support progress toward achieving Zero Hunger (SDG 2) in rural communities.
Integrating Professional Standards into Project-Based Learning: Evaluating an Innovative Model for Water Engineering Education in Indonesia
Full text linkEngineering education increasingly demands instructional approaches that connect academic knowledge with the professional competencies required in contemporary engineering practice. Responding to this need, the present study aimed to evaluate the effectiveness of a Profession-Oriented Project-Based Learning (PO-PBL) model that integrates professional engineering standards into the Water Infrastructure Design course at Universitas Ekasakti, Padang, Indonesia. The PO-PBL model embeds technical, ethical, and managerial competencies throughout the project cycle to strengthen students’ conceptual mastery and professional readiness. Using a quasi-experimental nonequivalent control group design, the study involved 65 undergraduate students divided into an experimental class (n = 32) and a control class (n = 33). Data were collected through 40-item pretests and posttests, a 20-item Likert-scale survey on engagement and professional perception, and a rubric-based evaluation of project performance. Statistical analyses, including paired t-tests, independent t-tests, and analysis of covariance, indicated that students in the experimental class achieved higher posttest scores, greater learning gains, and stronger project outcomes than those in the control class. Survey responses also revealed higher levels of motivation, collaboration, and perceived professional relevance among students exposed to the PO-PBL model. These results suggest that integrating professional standards into project-based learning enhances the authenticity and purposefulness of instructional activities, thereby improving both cognitive achievement and professional competence. Overall, the PO-PBL model offers a practical and scalable framework for aligning engineering education with national professional expectations and supporting the development of industry-ready water engineering graduates in Indonesia
Technological Integration and Soft Skill Synergy in Vocational Education: A Data-Driven Model for Enhancing Hairdressing Work Competence
Full text linkVocational education increasingly requires the integration of technical proficiency with adaptive interpersonal skills to prepare graduates for digitally oriented service industries. This study aimed to develop a data-driven model explaining how technological integration, soft skills, motivation, learning environment, and internship experience influence work competence among hairdressing students. A quantitative descriptive–correlational design was employed involving 237 students from four Indonesian vocational schools, using validated questionnaires and performance-based assessments. Descriptive findings indicate high levels of technical mastery (M = 82.4) and soft skills (M = 80.7), supported by very high motivation (M = 84.3) and internship experience (M = 85.6). Correlation results show significant associations between work competence and soft skills (r = 0.64), motivation (r = 0.71), and internship experience (r = 0.69). Multiple regression analysis identifies motivation (β = 0.42, p < 0.01) and internship experience (β = 0.37, p < 0.05) as the strongest predictors, with additional contributions from soft skills (β = 0.31, p < 0.05) and learning facilities (β = 0.28, p < 0.05), explaining 72% of variance in competence (R² = 0.72). Extended analysis shows that internships exceeding three months improved technical performance by 40 %, while technology-supported learning enhanced autonomy, reflective practice, and collaborative engagement. The findings demonstrate that vocational competence is a multidimensional construct shaped by psychological, experiential, and institutional factors. Integrating digital tools with structured soft skill development strengthens employability readiness, supports reflective and adaptive learning processes, and provides a comprehensive framework for preparing graduates to perform effectively in creative, client-centered professional environments
The implementation of authentic learning using a science pedagogical module based on augmented reality in science subjects for high school students
Full text linkThis study investigates the implementation of authentic learning through the development of an Augmented Reality (AR)-based science pedagogical module for secondary school students. The module was designed to enhance students’ conceptual understanding and learning motivation through interactive mobile technology. A quasi-experimental design was employed involving experimental and control groups, with data obtained from tests, questionnaires, and expert validation. The AR-based module demonstrated high validity with an expert score of 88,3 % and received positive student responses with a satisfaction rate of 87,5 %. Students in the experimental group achieved substantial improvement, as reflected in post-test scores increasing from 66,92 to 87,50. Statistical analyses confirmed significant differences between groups, and regression results indicated that the module contributed 92,9 % to learning outcomes. These findings suggest that the AR-based science module is valid, practical, and effective in facilitating authentic learning through immersive and interactive educational experiences
The Role of Artificial Intelligence Applications in Improving Research Competencies among Graduate Students
Full text linkThis study aimed to determine the degree of contribution of artificial intelligence (AI) applications to developing research skills and improving the quality of scientific research among postgraduate students at the University of Nizwa. It also sought to identify the university’s role in supporting research quality, highlight the challenges facing the development of research skills, and explore the risks that AI applications may pose to research quality. The study sample consisted of (108) postgraduate students, and a correlational descriptive method was employed. Data were collected using a questionnaire comprising (45) items distributed across four dimensions. The results showed that the degree of contribution of AI applications to developing research skills and improving the quality of scientific research among postgraduate students was high. The findings also indicated that the degree of contribution of the University of Nizwa to supporting research quality was high, while the challenges facing the development of research skills were moderate. In contrast, the risks associated with AI use were found to have a high impact on research quality
Educative exclusion in ecuador: identification of priority groups through strategic multivariate analysis for the formulation of public policies
Full text linkThis multivariate study analyzes educational attendance patterns in Ecuador, using data from the 2022 Population and Housing Census. The main purpose is to identify the age group with the highest incidence of educational exclusion, in order to guide targeted interventions. The research is classified as descriptive and exploratory, with a quantitative approach. The universe is composed of the population surveyed at the national level, and the unit of analysis corresponds to five-year age groups. The variables used were the total number of attendees, non-attendees, and total population, disaggregated by sex. The analysis integrates techniques such as graphical representation using principal components, hierarchization by Pareto analysis, and segmentation by hierarchical clusters, represented in heat maps. These tools made it possible to identify patterns of dependency and similarity between age groups, as well as to establish priorities for intervention. Among the main results, it is noteworthy that the 30-49 age groups account for more than 50% of the population that does not attend educational institutions, suggesting a structural lag. The segmentation confirms the existence of different profiles between age groups. It is concluded that the proposed approach facilitates the identification of priority cohorts, providing useful evidence for the formulation of educational policies aimed at reintegrating adults into the education system
Change of eating habits in pregnant women with overweight and obesity through a nutritional educational intervention
Full text linkIntroduction: Excess malnutrition is one of the main pandemics affecting women during the gestational stage, leading to adverse effects for both the mother and the fetus. Thus, the objective of the present study was to evaluate the effect of a nutritional educational intervention for changing dietary habits in pregnant women with overweight and obesity in the city of Ambato, EcuadorMethods: The intervention followed a pre-experimental single-sample design, with 49 pregnant women participating. For the diagnosis, a nutritional screening was conducted using the forms MSP/DNEAIS/form.051/May/2016 and SNS – MSP/HCU- Form51A-2011/IESS, along with anthropometric measurements. Three educational sessions lasting 30 to 45 minutes were then held through workshops and counseling, addressing three topics: consumption of sugary foods during pregnancy, water intake, and a healthy diet based on fruits and vegetables. Results: the 94,9 % of the participants were overweight or obese at any trimester of pregnancy. It was noted that the consumption of healthy foods among participants increased from 20 % to 49 % after the intervention, and knowledge about the risks of being overweight rose from 40,8 % to 81,6 %. Conclusions: It is concluded that a nutritional intervention for women in the gestational stage fosters the acquisition of new knowledge and healthy eating habits, which can prevent the development of gestational diabetes and other serious diseases for both the mother and the fetus
Method with machine learning to carry out feasibility in data mining projects
Full text linkCurrently, there are Data Mining techniques aimed at increasing the accuracy of the information and the agility in the analysis. These are applied in the productive sector to characterize behaviors, based on the discovery of knowledge and, in this way, base decision-making in real and dynamic situations. Artificial intelligence (AI) drives research methods and data mining techniques for knowledge acquisition. For its use, the life cycle of data mining projects is followed, which involves stages of extraction, cleaning, preparation and transformation, modeling, and data evaluation. However, it is important to consider a feasibility study for data mining projects, with the objective of positively impacting organizations, by minimizing costly errors and guaranteeing an efficient distribution of resources, as well as the decision on the continuity of a project. This article presents a Machine Learning method to carry out feasibility in data mining projects, seeking to impact organizations by minimizing costly errors and guaranteeing an efficient distribution of resources
Transdisciplinary STEAM and Quality Control Approaches Elevating Vocational Education in Support of SDGs
Full text linkIntroduction: the rapid development of modern industries requires vocational education to equip students with both technical expertise and creative, interdisciplinary competencies. To meet these demands, learning models must integrate scientific and technological knowledge with systematic quality assurance. This study examines the relevance and importance of combining STEAM-based learning with Quality Control (QC) to enhance students’ readiness for industry-standard production.Method: this study employed a Pretest–Posttest Control Group Design involving 32 vocational students divided into experimental and control classes. The experimental class received STEAM-QC–based instruction, integrating scientific concepts, technological tools, engineering design, artistic creativity, and mathematical analysis while applying QC at every production stage. Data were collected through tests, observations, and product assessments, and analyzed using Shapiro–Wilk and Levene tests, N-Gain, and Two-Way ANOVA. A total of 42 scholarly sources supported the theoretical and methodological framework.Results: the STEAM-QC intervention strengthened students’ interdisciplinary understanding, improved their systematic work processes, and enhanced the feasibility of their final products. Learning outcomes showed higher improvement in the experimental group, and product evaluations demonstrated better performance in construction quality, aesthetics, and durability. The model also promoted more consistent decision-making and reflective practice during production.Conclusions: the integration of STEAM and QC provides an effective instructional model for vocational education by aligning academic learning with industrial quality standards. This approach supports the development of competent and innovative graduates and contributes to achieving SDG 4 (Quality Education) and SDG 9 (Industry, Innovation, and Infrastructure)
Real-World Effectiveness of Oral Hypoglycemic Agents in Young Adults with Type 2 Diabetes in Northern Chile
Full text linkIntroduction: the rising prevalence of type 2 diabetes mellitus (T2DM) among adults aged 25–40 years in Latin America has emerged as a significant public health challenge, driven by lifestyle and metabolic risk factors. Evaluating the real-world effectiveness of oral hypoglycemic agents within primary health care (PHC) systems is essential to inform therapeutic strategies and improve glycemic control in this population. This study aimed to assess metabolic outcomes and treatment associations among young adults with T2DM managed in PHC settings in northern Chile. Method : a retrospective longitudinal cohort study was conducted using 500 electronic medical records from patients aged 25–40 years diagnosed with T2DM and treated between 2021 and 2023 across eight family health centers (CESFAM) in Antofagasta, Chile. The primary outcome was glycosylated hemoglobin (HbA1c). Secondary analyses examined relationships between treatment type, body mass index (BMI), and comorbidities including dyslipidemia, hypertension, and depression. Results: among all patients, 71,6 % were overweight and 28,4 % obese. Comorbidities were documented as 57,4 %, dyslipidemia (43,5 %), hypertension (27,9 %), and depression (18,8 %). The most common therapies were metformin monotherapy (84 %) and metformin plus glibenclamide (14,8 %). Mean HbA1c values remained unchanged between 2021 (8,91 ± 0,57) and 2022 (8,92 ± 0,55) but improved significantly in 2023 (7,41 ± 0,28), although international glycemic targets were not met. Conclusions: oral hypoglycemic therapy in PHC settings was partially effective in improving glycemic control among young adults with T2DM. These findings underscore the need for broader pharmacological options, enhanced follow-up, and reinforcing patient education within Chile’s primary care system.