METHODS: A cross-sectional study was conducted among students from 13 dental schools across Malaysia using online questionnaires.
RESULTS: From 355 respondents, 93.5% obtained a high score of knowledge of COVID-19. Female respondents scored higher than males in perceived risks and preventive behaviors. Chinese respondents scored highest in knowledge, while Malay respondents had the highest perceived risk score. The mean preventive behavior score did not vary across ethnicity. On-campus students scored higher in knowledge and perceived risk whereas off-campus students practiced more preventive behaviors. Clinical students' knowledge score was higher than preclinical students. Final year students scored higher in knowledge and perceived risk compared to their juniors.
CONCLUSION: The majority of dental students have good knowledge and a high perceived risk of COVID-19, and they practiced most of the preventive behaviors. However, the latest information on this disease should be incorporated into dental schools' curriculums and updated periodically.
METHODS: ML algorithms logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) models were applied. Academic performance prediction in pre-clinical years was made using three input parameters: age during admission, pre-university Cumulative Grade Point Average (CGPA), and total matriculation semester. PCC was deployed to identify the correlation between pre-university CGPA and dental school grades. The proposed models' classification accuracy ranged from 29% to 57%, ranked from highest to lowest as follows: RF, SVM, DT, and LR. Pre-university CGPA was shown to be predictive of dental students' academic performance; however, alone they did not yield optimal outcomes. RF was the most precise algorithm for predicting grades A, B, and C, followed by LR, DT, and SVM. In forecasting failure, LR predicted three grades with the highest recall, SVM predicted two grades, and DT predicted one. RF performance was insignificant.
CONCLUSION: The findings demonstrated the application of ML algorithms and PCC to predict dental students' academic performance. However, it was limited by several factors. Each algorithm has unique performance qualities, and trade-offs between different performance metrics may be necessary. No definitive model stood out as the best algorithm for predicting student academic success in this study.
PURPOSE: The purpose of this clinical study was to evaluate the effectiveness of traditional chairside practice and WhatsApp in improving patient knowledge of denture care and their awareness of the impact of edentulism on general health.
MATERIAL AND METHODS: Sixty-two participants who attended the Polyclinic Kulliyyah of Dentistry, IIUM Kuantan in 2022 for removable prosthesis fabrication were recruited. The participants were randomized into 2 groups: control (traditional chairside) and intervention (WhatsApp) group. Video intervention was sent via WhatsApp to the participants. Pretreatment and posttreatment questionnaires were distributed from March to September 2022 to survey their sociodemographic data, knowledge of denture care, and awareness of the effect of edentulism on general health. Data were obtained and checked for normality using the Shapiro-Wilk test. Data were analyzed using the Mann-Whitney U and Wilcoxon-Paired Signed-Rank tests (α=.05).
RESULTS: Eighty-two percent of participants favored WhatsApp as a tool for receiving information and used it daily (66.1%). The level of overall knowledge and awareness increased in groups after denture insertion instruction. Participants' knowledge of denture care (P=.001) and awareness of the effect of edentulism on general health (P=.001) improved significantly in the WhatsApp intervention group compared with the control group.
CONCLUSIONS: WhatsApp can be used as an alternative tool for improving denture care knowledge among denture wearers; increased awareness was observed with WhatsApp compared with the traditional chairside approach.