International Journal of Public Health and Health Sciences

Changing Educational Strategies in Nursing Colleges from the COVID-19 Pandemic : Experiences from Thailand and Japan

Atchara Meenasantirak — 2025-12-20

This article examines the impact of the COVID-19 pandemic on nursing education in Thailand and Japan, focusing on the adaptations in teaching methods and educational strategies implemented during the crisis. The study compares case studies from Srimahasarakam Nursing College in Thailand and Okayama Saiseikai Nursing College in Japan, highlighting significant shifts in educational management. Both institutions transitioned to online teaching for theoretical courses and emphasized infection control in laboratory-based training. Despite challenges, the innovative strategies employed contributed to a 100% national exam pass rate in Japan, demonstrating the effectiveness of these adaptations. The study explores similarities and differences in teaching methods, including blended learning, simulation-based training, and policy support. Findings indicate that the pandemic quickened educational transformation, integrating technology and adaptive learning techniques into nursing curricula. Understanding these adaptations provides valuable insights for future educational strategies in healthcare crisis management.

Reliability and Validity of The Adapted Thai Leeds Dependence Questionnaire (LDQ) in Sexual Minority Adults in Bangkok

Thawatwong Limthongsittikun — 2026-04-20

Substance use among sexual minority adults in Thailand poses significant risks. This study aimed to translate and validate the Leeds Dependence Questionnaire (LDQ) into Thai, providing a culturally sensitive tool for assessing substance dependence in this population. This quantitative study aimed to examine the psychometric properties of the Thai version of the LDQ among sexual minority adults. A total of 450 participants in Bangkok were recruited using non-probability sampling methods, specifically convenience and snowball sampling, to effectively access this target population. The instrument’s reliability and validity were evaluated using Cronbach's alpha, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and convergent validity tests.

The Thai version of the LDQ demonstrated high reliability (α=.93) and validity. EFA identified a single-factor structure, which was further confirmed by CFA. The CFA results, applying modification indices, showed χ²=74.808 (p<.001), df=27, χ²/df=2.771, CFI=0.983, TLI=0.972, GFI=0.956, RMSEA=0.077, and PNFI=0.584. Additionally, convergent validity was established with a Composite Reliability (CR) of 0.953 and an Average Variance Extracted (AVE) of 0.671. The Thai adaptation of the LDQ is a reliable and valid tool for assessing substance dependence among sexual minority adults in Bangkok. CFA confirmed the model's fit, with most indices within acceptable ranges, supporting the tool's structural integrity. This adaptation enhances self-assessment and facilitates timely access to treatment. Future research should focus on validating the LDQ in diverse cultural settings to improve its applicability and effectiveness.

Kernel Function Exploration in Support Vector Machine for Digit Handwritten Recognition

Natthapat Markpradit — 2026-04-20

Handwriting classification plays a vital role in biomedical informatics, particularly for digitizing handwritten records and automating data entry. This technology can significantly benefit Thai public health by improving the efficiency of medical record digitization enhancing disease surveillance and outbreak response, and supporting research and development. These applications can lead to better healthcare access, improved patient outcomes, and more impactful health promotion and disease prevention efforts. This study aims to compare the performance of the difference kernel functions in Support Vector Machine, including linear kernel, polynomial kernel and Radial Basis Function (RBF) apply to the MNIST dataset, and the benchmark dataset EMNIST Digits, the extended size of MNIST. This study investigates the efficacy of three kernel function in Support Vector Machine (SVM) classification models including linear kernel, polynomial kernel and Radial Basis Kernel (RBF) to recognize digit handwriting. These classification methods initially to fitting and validate in “MNIST” as the starting dataset. The performance metrices resulting from these methods will be calculated together with the loss function to obtain the result of error analysis. These methods also will be implemented in “EMNIST Digits”, the extended dataset of MNIST as a generalization indicator. The tools for running model of training part and test part evaluate by Python programming (Google Colab Pro), supported by high-RAM NVIDIA T4 and/or GPU hardware accelerators to enhance processing efficiency. The result of model performance metrices analysis from R programming for statistical computation.

The results from three repeated measurements revealed that the average test accuracy of SVM by using linear kernel, polynomial kernel and RBF on the MNIST and EMNIST Digits datasets achieved as 94.28% and 91.90%, 98.42% and 99.14%, 98.67% and 99.25%, respectively, whereas hyperparameter tunning execution time on the MNIST and EMNIST Digits datasets are 50.61,2084.86, 556.99 and 62.93, 654.94,3936.94 seconds respectively.The results revealed that the linear kernel of SVM achieved the shortest execution time during hyperparameter tuning, making it suitable for applications with limited computational resources. In contrast, the RBF kernel of SVM demonstrated the highest classification accuracy but required significantly longer processing time. Notably, the polynomial kernel offered a balanced trade-off, yielding competitive performance with moderate computational demands. This study highlights the importance of selecting an appropriate kernel function based on the specific requirements of the task, such as accuracy, interpretability, and computational efficiency.

Effects of a Health Promotion Program on Health Behavior in Working-age Adults

Apichead Poonsub — 2026-04-30

This quasi-experimental one-group pretest-posttest design aimed to examine the effects of a health promotion program on health behaviors among working-age adults. This study openly recruited 45 participants who are supporting staff in a nursing college. All participants had received training in a health promotion program that applied the conceptual framework of Pender’s Health Promotion Model along with a 12-week program. The research instrument was a health promotion program (CVI=1) and a health behavior questionnaire based on the 3Es 2Ss principle (CVI=1, reliability=0.86). Data were analyzed using descriptive analysis: frequency, percentage, mean, standard deviation, and Wilcoxon signed-rank test. The results revealed that after participation in the program, participants demonstrated significantly higher mean health behavior scores (Mean = 3.93, SD = 0.238) compared with pre-intervention scores (Mean = 3.65, SD = 0.227), with statistical significance (p<0.05). Specifically, mean scores for dietary behavior, physical activity, stress management, and alcohol consumption were significantly higher after the intervention (p < 0.05). In contrast, smoking behavior did not show a statistically significant change (p = 0.25). The finding indicated that the health promotion program was associated with improvement in health behavior among supporting staff; however, smoking cessation may require longer follow-up periods or sustained motivational support to facilitate successful quitting.