AIMS: To describe the national state of advance care planning development in Malaysia METHODS: Review of relevant advance care planning literature locally and internationally was undertaken.
RESULTS: Positive development in Malaysia includes implementation of advance care planning at institutional level, initiatives to develop educational programmes as well as research activities to understand the attitude and perception of patients on advance care planning. However, there remain challenges, including lack of knowledge and awareness, lack of legislative framework to guide advance care planning implementation and lack of strong initiatives at a national level.
CONCLUSIONS: It is evident that there is much to learn nationally and internationally about ACP before any decision on implementation of ACP is made in Malaysia. ACP is a public health issue and requires concerted effort of all stakeholders, including Government agencies, academic institutions, and non-government organizations to raise public awareness. More research is needed to shape the future direction of ACP development in Malaysia.
METHODS: Using the Mechanics-Dynamics-Aesthetics' (MDA) framework, a new, tutorless educational board game known as the Simulated Disaster Management And Response Triage training ("SMARTriage") was first developed for disaster response training. Subsequently, the perceptions of 113 final year medical students on the "SMARTriage" board game was compared with that of tabletop exercise using a crossover design.
RESULTS: Using Wilcoxon signed rank test, it was that found that tabletop exercise was generally rated significantly higher (with p
METHODS: EEG single trials are decomposed with discrete wavelet transform (DWT) up to the [Formula: see text] level of decomposition using a biorthogonal B-spline wavelet. The coefficients of DWT in each trial are thresholded to discard sparse wavelet coefficients, while the quality of the signal is well maintained. The remaining optimum coefficients in each trial are encoded into bitstreams using Huffman coding, and the codewords are represented as a feature of the ERP signal. The performance of this method is tested with real visual ERPs of sixty-eight subjects.
RESULTS: The proposed method significantly discards the spontaneous EEG activity, extracts the single-trial visual ERPs, represents the ERP waveform into a compact bitstream as a feature, and achieves promising results in classifying the visual objects with classification performance metrics: accuracies 93.60[Formula: see text], sensitivities 93.55[Formula: see text], specificities 94.85[Formula: see text], precisions 92.50[Formula: see text], and area under the curve (AUC) 0.93[Formula: see text] using SVM and k-NN machine learning classifiers.
CONCLUSION: The proposed method suggests that the joint use of discrete wavelet transform (DWT) with Huffman coding has the potential to efficiently extract ERPs from background EEG for studying evoked responses in single-trial ERPs and classifying visual stimuli. The proposed approach has O(N) time complexity and could be implemented in real-time systems, such as the brain-computer interface (BCI), where fast detection of mental events is desired to smoothly operate a machine with minds.
EDUCATIONAL ACTIVITY AND SETTING: We present the approach to remote extemporaneous compounding teaching taken by three pharmacy schools: Monash University Malaysia, University of Michigan, and University of Maryland. Prior to delivery, students were either supplied with or asked to procure a set of easily accessible ingredients and equipment to conduct the extemporaneous practicals from home. We conducted lessons remotely using both synchronous and asynchronous delivery, and demonstrated, taught, and assessed practical lab skills using video conferencing modalities.
FINDINGS: We successfully conducted remote teaching of extemporaneous compounding, where similar learning outcomes to the face-to-face implementation were achieved. At Monash University Malaysia, > 90% of students responding to the post-activity surveys found the remote extemporaneous sessions useful for their learning, and qualitative comments supported these views. Mean scores from the remote extemporaneous labs in 2021 were similar to those when conducted physically in 2019, supporting the effectiveness of the approach. The different approaches attempted by the three institutions highlighted the flexibility in implementation that can be considered to achieve similar outcomes.
SUMMARY: Combining technology-based approaches with synchronous and asynchronous teaching and learning methods can successfully deliver extemporaneous compounding skills remotely.
METHOD: An expert panel of educators was recruited and completed a literature review of current evidence of teaching and learning and assessment methods in healthcare training, with an emphasis on health care, general optometry and CLE.
RESULTS: No direct evidence of benefit of teaching and learning and assessment methods in CLE were found. There was evidence for the benefit of some teaching and learning and assessment methods in other disciplines that could be transferable to CLE and could help students meet the intended learning outcomes. There was evidence that the following teaching and learning methods helped health-care and general optometry students meet the intended learning outcomes; clinical teaching and learning, flipped classrooms, clinical skills videos and clerkships. For assessment these methods were; essays, case presentations, objective structured clinical examinations, self-assessment and formative assessment. There was no evidence that the following teaching and learning methods helped health-care and general optometry students meet the intended learning outcomes; journal clubs and case discussions. Nor was any evidence found for the following assessment methods; multiple-choice questions, oral examinations, objective structured practical examinations, holistic assessment, and summative assessment.
CONCLUSION: Investigation into the efficacy of common teaching and learning and assessment methods in CLE are required and would be beneficial for the entire community of contact lens educators, and other disciplines that wish to adapt this approach of evidence-based teaching.
METHOD: The paper explores a combination of variational mode decomposition (VMD), and Hilbert transform (HT) called VMD-HT to extract hidden information from EEG signals. Forty-one statistical parameters extracted from the absolute value of analytical mode functions (AMF) have been classified using the explainable boosted machine (EBM) model. The interpretability of the model is tested using statistical analysis and performance measurement. The importance of the features, channels and brain regions has been identified using the glass-box and black-box approach. The model's local and global explainability has been visualized using Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), Partial Dependence Plot (PDP), and Morris sensitivity. To the best of our knowledge, this is the first work that explores the explainability of the model prediction in ADHD detection, particularly for children.
RESULTS: Our results show that the explainable model has provided an accuracy of 99.81%, a sensitivity of 99.78%, 99.84% specificity, an F-1 measure of 99.83%, the precision of 99.87%, a false detection rate of 0.13%, and Mathew's correlation coefficient, negative predicted value, and critical success index of 99.61%, 99.73%, and 99.66%, respectively in detecting the ADHD automatically with ten-fold cross-validation. The model has provided an area under the curve of 100% while the detection rate of 99.87% and 99.73% has been obtained for ADHD and HC, respectively.
CONCLUSIONS: The model show that the interpretability and explainability of frontal region is highest compared to pre-frontal, central, parietal, occipital, and temporal regions. Our findings has provided important insight into the developed model which is highly reliable, robust, interpretable, and explainable for the clinicians to detect ADHD in children. Early and rapid ADHD diagnosis using robust explainable technologies may reduce the cost of treatment and lessen the number of patients undergoing lengthy diagnosis procedures.