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Fulltext Effectiveness of "Be SAFE Drowning Prevention and Water Safety Booklet" Intervention for Parents and Guardians

Farizan NH, Sutan R, Mani KK

Iran J Public Health, 2020 Oct;49(10):1921-1930.
PMID: 33346223 DOI: 10.18502/ijph.v49i10.4695

Background: We aimed to assess the effectiveness of the health educational booklet intervention in improving parents/guardian's knowledge on prevention of child drowning and, the perception of drowning risk and water safety practice.
Methods: A quasi-experimental study was conducted in year 2017 in Selangor, Malaysia among 719 parents/guardians of primary school children. The parent/guardians were randomly assigned as the intervention groups and were given a health educational Be-SAFE booklet on drowning prevention and water safety. The pretest was conducted before the intervention and posttest was done one month of intervention. The data collection tool was using a validated questionnaire on knowledge, attitude and practice for drowning prevention and water safety.
Results: There were 719 respondents (response rate of 89.9%) participated at baseline and 53.7% at end line (after the intervention). Significant differences found in knowledge, attitudes and practice on drowning prevention and water safety for the intervention and control groups after the intervention (P<0.001). There was a significant difference in mean scores for knowledge and attitude before and after the intervention, whereas no significant findings noted for practices (P<0.001).
Conclusion: Be SAFE booklet contributed to the increase in parents/guardian's knowledge and attitudes towards drowning prevention and water safety to prevent the risk of child drowning.
The analysis of teaching quality evaluation for the college sports dance by convolutional neural network model and deep learning

Guo S, Yang X, Farizan NH, Samsudin S

Heliyon, 2024 Aug 30;10(16):e36067.
PMID: 39224395 DOI: 10.1016/j.heliyon.2024.e36067

This study aims to comprehensively analyze and evaluate the quality of college physical dance education using Convolutional Neural Network (CNN) models and deep learning methods. The study introduces a teaching quality evaluation (TQE) model based on one-dimensional CNN, addressing issues such as subjectivity and inconsistent evaluation criteria in traditional assessment methods. By constructing a comprehensive TQE system comprising 24 evaluation indicators, this study innovatively applies deep learning technology to quantitatively assess the quality of physical dance education. This TQE model processes one-dimensional evaluation data by extracting local features through convolutional layers, reducing dimensions via pooling layers, and feeding feature vectors into a classifier through fully connected layers to achieve an overall assessment of teaching quality. Experimental results demonstrate that after 150 iterations of training and validation on the TQE model, convergence is achieved, with mean squared error (MSE) decreasing to 0.0015 and 0.0216 on the training and validation sets, respectively. Comparatively, the TQE model exhibits significantly lower MSE on the training, validation, and test sets compared to the Back-Propagation Neural Network, accompanied by a higher R2 value, indicating superior accuracy and performance in data fitting. Further analysis on robustness, parameter sensitivity, multi-scenario adaptability, and long-term learning capabilities reveals the TQE model's strong resilience and stability in managing noisy data, varying parameter configurations, diverse teaching contexts, and extended time-series data. In practical applications, the TQE model is implemented in physical dance courses at X College to evaluate teaching quality and guide improvement strategies for instructors, resulting in notable enhancements in teaching quality and student satisfaction. In conclusion, this study offers a comprehensive evaluation of university physical dance education quality through a multidimensional assessment system and the application of the 1D-CNN model. It introduces a novel and effective approach to assessing teaching quality, providing a scientific foundation and practical guidance for future educational advancements.

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