ORCID Profile
0000-0001-7786-4251
Current Organisation
Universiti Sains Malaysia - Kampus Kesihatan
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Publisher: Springer Science and Business Media LLC
Date: 14-01-2021
DOI: 10.1186/S12909-020-02467-W
Abstract: The Anatomy Education Environment Measurement Inventory (AEEMI) evaluates the perception of medical students of educational climates with regard to teaching and learning anatomy. The study aimed to cross-validate the AEEMI, which was previously studied in a public medical school, and proposed a valid universal model of AEEMI across public and private medical schools in Malaysia. The initial 11-factor and 132-item AEEMI was distributed to 1930 pre-clinical and clinical year medical students from 11 medical schools in Malaysia. The study examined the construct validity of the AEEMI using exploratory and confirmatory factor analyses. The best-fit model of AEEMI was achieved using 5 factors and 26 items (χ 2 = 3300.71 (df = 1680), P 0.001, χ 2 /df = 1.965, Root Mean Square of Error Approximation (RMSEA) = 0.018, Goodness-of-fit Index (GFI) = 0.929, Comparative Fit Index (CFI) = 0.962, Normed Fit Index (NFI) = 0.927, Tucker–Lewis Index (TLI) = 0.956) with Cronbach’s alpha values ranging from 0.621 to 0.927. Findings of the cross-validation across institutions and phases of medical training indicated that the AEEMI measures nearly the same constructs as the previously validated version with several modifications to the item placement within each factor. These results confirmed that variability exists within factors of the anatomy education environment among institutions. Hence, with modifications to the internal structure, the proposed model of the AEEMI can be considered universally applicable in the Malaysian context and thus can be used as one of the tools for auditing and benchmarking the anatomy curriculum.
Publisher: Frontiers Media SA
Date: 03-09-2019
Publisher: Springer Science and Business Media LLC
Date: 10-11-2010
DOI: 10.1186/S12889-020-09756-5
Abstract: Respiratory tract infections are one of the common infection associated with Hajj pilgrimage that is of great public health and global concern. This study is aimed at determining the factor structure of the knowledge, attitude, and practice questionnaire for the prevention of respiratory tract infections during Hajj by confirmatory factor analysis (CFA). A multistage cluster s ling method was conducted on Malaysian Umrah pilgrims during the weekly Umrah orientation course. A total of 200 Umrah pilgrims participated in the study. The knowledge, attitude and practice (KAP) questionnaire was distributed to pilgrims at the beginning of the orientation and retrieved immediately at the end of the orientation. Data analysis was done using R version 3.5.0 after data entry into SPSS 24. The robust maximum likelihood was used for the estimation due to the multivariate normality assumption violation. A two-factor model was tested for measurement model validity and construct validity for each of the attitude and practice domains. CFA of a 25-item in total, the two-factor model yielded adequate goodness-of-fit values. The measurement model also showed good convergent and discriminant validity after model re-specification. A two-factor model was tested for measurement model validity and construct validity for each of the attitude and practice domains. The result also showed a statistically significant value ( p 0.001) with χ 2 (df) values of 76.8 (43) and 121 (76) for attitude and practice domains, respectively. The KAP questionnaire was proven to have a valid measurement model and reliable constructs. It was deemed suitable for use to measure the KAP of Hajj and Umrah pilgrims towards the prevention for all respiratory tract infections.
Publisher: MDPI AG
Date: 18-11-2019
Abstract: Respiratory tract infection (RTI) is a major public health challenge during the Muslim pilgrimage to Makkah. This study aims to evaluate the knowledge, attitude, and practice of Malaysian Hajj and Umrah pilgrims towards the prevention of RTIs in 2018 and determine correlations among three domains. A cross-sectional study was conducted among 225 Umrah and Hajj pilgrims. Knowledge, attitude, and practice (KAP) towards RTI prevention was assessed by using a validated self-administered questionnaire among pilgrims attending a weekly orientation course organized by private Hajj/Umrah companies. Out of 225 participants, 65.9% of respondents were female with the mean (SD) age of 46.74 (13.38) years. The interquartile range (IQR) score for knowledge is 18.0 (6.0), the mean scores of attitude and practice are 32.65 (4.72) and 25.30 (4.9). respectively. Significant and negative linear correlations between knowledge and practice (r = −0.232, p 0.001), and attitude and practice (r = 0.134, p = 0.045) were observed. Results from the current study showed good knowledge of RTIs among Malaysian pilgrims. However, a poor attitude was reflected in their preventive practice behaviors. This will further help in the prevention and management of RTIs during Hajj and Umrah. Therefore, an extensive educational health c aign should be provided to pilgrims to create awareness.
Publisher: Penerbit Universiti Sains Malaysia
Date: 2020
Publisher: Penerbit Universiti Sains Malaysia
Date: 2019
Publisher: MDPI AG
Date: 06-03-2020
Abstract: Sports courage is one of the most important attributes to help competitive athletes overcome anxiety, nervousness, and other psychological obstacles, but this field of study is still being overlooked by most athletes and coaches. The purpose of this study is to validate the Malay language version of the Sports Courage Scale (SCS-M) for Silat athletes using confirmatory factor analysis (CFA). Data were collected during 9th UPSI International Pencak Silat Ch ionship in Malaysia. A total of 258 competitors (male = 66.7%, female = 33.3%), with a mean age of 18 years (SD = 2.6), volunteered to participate in this study. The original SCS with 50 items underwent forward and backward translations into the Malay language and was pre-tested with ten martial arts athletes. Then, Silat athletes were asked to complete the translated SCS-M questionnaire. There were five factors in the SCS-M (i.e., mastery, determination, assertiveness, venturesome, and self-sacrificial behaviour). The first hypothesised model with 50 items did not result in a good fit to the data (RMSEA = 0.06, CFI = 0.93, NFI = 0.87, NNFI = 0.93, RMR = 0.14, SRMR = 0.09). A total of 17 problematic items were identified and were removed iteratively. The final measurement model with 33 items fit the data well (RMSEA = 0.06, CFI = 0.94, NFI = 0.89, NNFI = 0.94, RMR = 0.05, SRMR = 0.07). The reliability of each subscale based on Cronbach’s alpha ranged from 0.64 to 0.76. The convergent and discriminant validities were achieved for the final measurement model. The revised version of SCS-M with 33 items was considered valid and reliable for measuring the sports courage in Silat athletes in Malaysia.
Publisher: MDPI AG
Date: 16-11-2022
DOI: 10.3390/DIAGNOSTICS12112826
Abstract: This study aims to determine the feasibility of machine learning (ML) and patient registration record to be utilised to develop an over-the-counter (OTC) screening model for breast cancer risk estimation. Data were retrospectively collected from women who came to the Hospital Universiti Sains Malaysia, Malaysia for breast-related problems. Eight ML models were used: k-nearest neighbour (kNN), elastic-net logistic regression, multivariate adaptive regression splines, artificial neural network, partial least square, random forest, support vector machine (SVM), and extreme gradient boosting. Features utilised for the development of the screening models were limited to information in the patient registration form. The final model was evaluated in terms of performance across a mammographic density. Additionally, the feature importance of the final model was assessed using the model agnostic approach. kNN had the highest Youden J index, precision, and PR-AUC, while SVM had the highest F2 score. The kNN model was selected as the final model. The model had a balanced performance in terms of sensitivity, specificity, and PR-AUC across the mammographic density groups. The most important feature was the age at examination. In conclusion, this study showed that ML and patient registration information are feasible to be used as the OTC screening model for breast cancer.
Publisher: MDPI AG
Date: 16-04-2020
Abstract: Decisional balance (DB) is the perceived positive aspects (advantages) and negative aspects (disadvantages) that are associated with behavioural change. Behavioural change is dependent on an in idual’s thoughts after considering the advantages of engaging in exercise. When the benefits exceed the barriers, people are more likely to make changes after cognitively evaluating the functional aspects. The purpose of the present study is to determine the validity and reliability of the DB scale among Malaysian university students using a confirmatory factor analysis (CFA). A cross-sectional study was carried out among students who took part in the co-curricular program. By using the purposive s ling method, students were recruited and given written informed consent forms after acknowledging they understood the purpose of the study. The DB scale, which consists of two factors, namely, advantages and disadvantages, was used as the instrument in the study. The advantages referred to the benefits of participating in exercise, whereas the disadvantages referred to the barriers to exercise. The 10-item, self-administered questionnaires were distributed to participating students. Data were analysed using Mplus 8 for the CFA. A total of 562 students (females = 444, males = 118) with a mean age of 19.81 years (SD = 1.22) participated in the study. Most of the students were engaged in regular physical activity for at least three exercise sessions (mean = 2.62) per week, and the average duration per session was 43 minutes. The hypothesised measurement model of DB did not fit the data well thus, the measurement model was re-specified. The final measurement model fit the data well (comparative fit index (CFI) = 0.960, Tucker–Lewis index (TLI) = 0.943, standardised root mean square residual (SRMR) = 0.055, root mean square error of approximation (RMSEA) (90% confidence interval (CI)) = 0.061 (0.047, 0.074), RMSEA p-value = 0.096). The composite reliability values of 0.757 for the advantages and 0.792 for the disadvantages were acceptable. The 10-item DB scale with two factors displayed a good model fit for the data with good scale reliability. This could be beneficial for Malaysian undergraduate students in making decisions before engaging in physical activity. The benefits of, and barriers to, exercise could be an important component that affects their decision making.
Publisher: Frontiers Media SA
Date: 17-12-2018
Publisher: MDPI AG
Date: 03-2023
DOI: 10.20944/PREPRINTS202303.0015.V1
Abstract: This study utilised an ensemble of pre-trained networks and digital mammograms to develop a supplementary diagnostic tool for radiologists. Digital mammograms and their associated information were collected from the department of radiology and pathology, Hospital Universiti Sains Malaysia. Thirteen pre-trained networks were selected and explored in this study. ResNet101V2 and ResNet152 had the highest mean PR-AUC, MobileNetV3Small and ResNet152 had the highest mean precision, ResNet101 had the highest mean F1 score, and ResNet152 and ResNet152V2 had the highest mean Youden J index. Subsequently, three ensemble models were developed using the top three pre-trained networks based on PR-AUC, precision, and F1 score. The final ensemble model had a mean precision, F1 score, and Youden J index of 0.82, 0.68, and 0.12, respectively. Additionally, the final model demonstrated a balanced performance across mammographic density. In conclusion, this study exhibited the good performance of the ensemble transfer learning on digital mammograms for the purpose of breast cancer risk estimation. This model can be utilised as a supplementary diagnostic tool for radiologists, thus, reducing their workloads.
Publisher: Frontiers Media SA
Date: 18-09-2019
Publisher: MDPI AG
Date: 30-03-2022
DOI: 10.3390/DIAGNOSTICS12040860
Abstract: Mammographic density is a significant risk factor for breast cancer. In this study, we identified the risk factors of mammographic density in Asian women and quantified the impact of breast density on the severity of breast cancer. We collected data from Hospital Universiti Sains Malaysia, a research- and university-based hospital located in Kelantan, Malaysia. Multivariable logistic regression was performed to analyse the data. Five significant factors were found to be associated with mammographic density: age (OR: 0.94 95% CI: 0.92, 0.96), number of children (OR: 0.88 95% CI: 0.81, 0.96), body mass index (OR: 0.88 95% CI: 0.85, 0.92), menopause status (yes vs. no, OR: 0.59 95% CI: 0.42, 0.82), and BI-RADS classification (2 vs. 1, OR: 1.87 95% CI: 1.22, 2.84 3 vs. 1, OR: 3.25 95% CI: 1.86, 5.66 4 vs. 1, OR: 3.75 95% CI: 1.88, 7.46 5 vs. 1, OR: 2.46 95% CI: 1.21, 5.02 6 vs. 1, OR: 2.50 95% CI: 0.65, 9.56). Similarly, the average predicted probabilities were higher among BI-RADS 3 and 4 classified women. Understanding mammographic density and its influencing factors aids in accurately assessing and screening dense breast women.
Publisher: Springer Science and Business Media LLC
Date: 02-03-2020
DOI: 10.1186/S12889-020-8269-9
Abstract: Hajj pilgrimage faces numerous challenges including a high prevalence of respiratory tract infection as well as its prevention strategies. The aim of this study was to develop and validate a questionnaire to evaluate knowledge, attitude and practice (KAP) towards respiratory tract infections (RTIs) prevention among Malaysian Hajj pilgrims. This study was conducted among Malaysian Umrah pilgrims in Malaysia from Kuala Lumpur and Kelantan. The questionnaire then underwent a series of validation process that included content, face validity and exploratory part. Item response theory (IRT) analysis was utilized for the validation of the knowledge domain. The attitude and practice were validated using the exploratory factor analysis (EFA). The validation process resulted in a questionnaire that comprised of four main sections: demography, knowledge, attitude, and practice. Following IRT analysis of the knowledge domain, all items analyzed were within the acceptable range of difficulty and discrimination. The Kaiser-Meyer-Olkin measure of s ling adequacy (KMO) was 0.72 and 0.84 for attitude and practice domain respectively and Bartlett’s test of Sphericity for both domains were highly significant ( P 0.001). The factor analysis resulted in two factors with total of 12 items in attitude domain, and 2 factors with total of 13 items in the practice domain with satisfactory factor loading ( 0.3). The Cronbach’s alpha for reliability of the knowledge, attitude and practice domains all showed acceptable values of 0.6 (0.92, 0.77 and 0.85). The findings of this validation and reliability study showed that the developed questionnaire had a satisfactory psychometric property for measuring KAP of Malaysian Hajj pilgrims.
Publisher: Public Library of Science (PLoS)
Date: 28-09-2020
Publisher: Wiley
Date: 30-01-2017
DOI: 10.1002/ASE.1683
Abstract: Students' perceptions of the education environment influence their learning. Ever since the major medical curriculum reform, anatomy education has undergone several changes in terms of its curriculum, teaching modalities, learning resources, and assessment methods. By measuring students' perceptions concerning anatomy education environment, valuable information can be obtained to facilitate improvements in teaching and learning. Hence, it is important to use a valid inventory that specifically measures attributes of the anatomy education environment. In this study, a new 11-factor, 132-items Anatomy Education Environment Measurement Inventory (AEEMI) was developed using Delphi technique and was validated in a Malaysian public medical school. The inventory was found to have satisfactory content evidence (scale-level content validity index [total] = 0.646) good response process evidence (scale-level face validity index [total] = 0.867) and acceptable to high internal consistency, with the Raykov composite reliability estimates of the six factors are in the range of 0.604-0.876. The best fit model of the AEEMI is achieved with six domains and 25 items (X
Publisher: MDPI AG
Date: 18-05-2010
DOI: 10.3390/DIAGNOSTICS13101780
Abstract: Breast cancer is the most prevalent cancer worldwide. Thus, it is necessary to improve the efficiency of the medical workflow of the disease. Therefore, this study aims to develop a supplementary diagnostic tool for radiologists using ensemble transfer learning and digital mammograms. The digital mammograms and their associated information were collected from the department of radiology and pathology at Hospital Universiti Sains Malaysia. Thirteen pre-trained networks were selected and tested in this study. ResNet101V2 and ResNet152 had the highest mean PR-AUC, MobileNetV3Small and ResNet152 had the highest mean precision, ResNet101 had the highest mean F1 score, and ResNet152 and ResNet152V2 had the highest mean Youden J index. Subsequently, three ensemble models were developed using the top three pre-trained networks whose ranking was based on PR-AUC values, precision, and F1 scores. The final ensemble model, which consisted of Resnet101, Resnet152, and ResNet50V2, had a mean precision value, F1 score, and Youden J index of 0.82, 0.68, and 0.12, respectively. Additionally, the final model demonstrated balanced performance across mammographic density. In conclusion, this study demonstrates the good performance of ensemble transfer learning and digital mammograms in breast cancer risk estimation. This model can be utilised as a supplementary diagnostic tool for radiologists, thus reducing their workloads and further improving the medical workflow in the screening and diagnosis of breast cancer.
No related grants have been discovered for Wan Nor Arifin.