ORCID Profile
0000-0002-8787-8128
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Publisher: Academy of Sciences Malaysia
Date: 24-06-2020
Abstract: Simulation-based medical education consistently finds debriefing to be the most important element in providing effective learning. Yet, there are limited studies that demonstrate the outcomes of debriefing on simulation-based resuscitation learning in the non-medical community. This mixed-method study examined the effects of debriefing methods (DIAMOND vs Customary) in 2 simulation experiences on 130 cabin crews' resuscitation knowledge, technical & non-technical skills. The quality of debriefing was assessed using a survey followed by analysis through face interview. The findings showed that there was no significant effect on the usage of different debriefing method by both groups on the retention of all variables, F (3,123) = .540, p = .656, partial h2 = .013. The DIAMOND debriefing was showed to be more quality as perceived by the DASH-SV scores, t = -6.244, df = 98, p = .001. Elements such as Cognitive, Methodology & Psychosocial were reported to promote the retention of knowledge and skills among the participants. Despite not generating a statistically significant difference, this study reports important information about the influence of structured debriefing with additional investigations conducted with improved designs are needed to provide further evidence and perceptual effectiveness of structured debriefing.
Publisher: Wiley
Date: 20-01-2021
DOI: 10.1111/ADD.15346
Abstract: To assess the effectiveness of training stop smoking services providers in Malaysia to deliver support for smoking cessation based on the UK National Centre for Smoking Cessation and Training (NCSCT) standard treatment programme compared with usual care. Two‐arm cluster‐randomized controlled effectiveness trial across 19 sites with follow‐up at 4‐week, 3‐month, and 6‐month. Stop smoking services operating in public hospitals in Malaysia. Five hundred and two smokers [mean ± standard deviation (SD), age 45.6 (13.4) years 97.4% male] attending stop smoking services in hospital settings in Malaysia: 330 in 10 hospitals in the intervention condition and 172 in nine hospitals in the control condition. The intervention consisted of training stop‐smoking practitioners to deliver support and follow‐up according to the NCSCT Standard Treatment Programme. The comparator was usual care (brief support and follow‐up). The primary outcome was continuous tobacco smoking abstinence up to 6 months in smokers who received smoking cessation treatment, verified by expired‐air carbon monoxide (CO) concentration. Secondary outcomes were continuous CO‐verified tobacco smoking abstinence up to 4 weeks and 3 months. Follow‐up rates at 4 weeks, 3 months and 6 months were 80.0, 70.6 and 53.3%, respectively, in the intervention group and 48.8, 30.8 and 23.3%, respectively, in the control group. At 6‐month follow‐up, 93 participants in the intervention group and 19 participants in the control group were abstinent from smoking, representing 28.2 versus 11.0% in an intention‐to‐treat (ITT) analysis assuming that participants with missing data had resumed smoking, and 52.8 versus 47.5% in a follow‐up‐only (FUO) analysis. Unadjusted odds ratios (accounting for clustering) were 5.04, (95% confidence interval (CI) = 1.22–20.77, P = 0.025) and 1.70, (95% CI = 0.25–11.53, P = 0.589) in the ITT and FUO analyses, respectively. Abstinence rates at 4 week and 3 month follow‐ups were significantly higher in the intervention versus control group in the ITT but not the FUO analysis. On an intention‐to‐treat analysis with missing‐equals‐smoking imputation, training Malaysian stop smoking service providers in the UK National Centre for Smoking Cessation and Training standard treatment programme appeared to increase 6 month continuous abstinence rates in smokers seeking help with stopping compared with usual care. However, the effect may have been due to increasing follow‐up rates.
Publisher: Oxford University Press (OUP)
Date: 25-05-2021
Abstract: This qualitative study explores the medical radiation workers’ (MRWs) beliefs with the support of the theory of planned behaviour’s constructs regarding the use of personal dosimeters in order to identify the facilitating factors and barriers to practising good personal dose monitoring. The exploration was conducted through semi-structured face-to-face interviews with 63 MRWs from the public, private, and university hospitals. Belief statements from the informants were organized under the behavioural, normative, and control belief, as guided by the theory. A thematic analysis found that a majority of informants acknowledged the benefits of using dosimeters. However, several factors influenced the actual usage. The informants were hesitant to use the dosimeter as the loss of the device involved an expensive penalty. They also mentioned that delayed dosimeter supplies due to late budget approval in the hospitals and some other reasons had got them disconnected from the monitoring system. The workers’ attitudes and social norms highly induced their dosimeter usage as well some perceived themselves to be at low risk for high exposure to radiation, and forgetfulness was also mentioned as a reason for lack of adherence. Device physical factor influenced low dosimeter use too. This study highlighted some unique findings in Asian settings. A better understanding of the underlying reasons for the lack of dosimeter use will be useful in developing strategies to increase good practices in personal radiation monitoring.
Publisher: JMIR Publications Inc.
Date: 07-12-2022
DOI: 10.2196/40404
Abstract: Overweight or obesity is a primary health concern that leads to a significant burden of noncommunicable disease and threatens national productivity and economic growth. Given the complexity of the etiology of overweight or obesity, machine learning (ML) algorithms offer a promising alternative approach in disentangling interdependent factors for predicting overweight or obesity status. This study examined the performance of 3 ML algorithms in comparison with logistic regression (LR) to predict overweight or obesity status among working adults in Malaysia. Using data from 16,860 participants (mean age 34.2, SD 9.0 years n=6904, 41% male n=7048, 41.8% with overweight or obesity) in the Malaysia’s Healthiest Workplace by AIA Vitality 2019 survey, predictor variables, including sociodemographic characteristics, job characteristics, health and weight perceptions, and lifestyle-related factors, were modeled using the extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM) algorithms, as well as LR, to predict overweight or obesity status based on a BMI cutoff of 25 kg/m2. The area under the receiver operating characteristic curve was 0.81 (95% CI 0.79-0.82), 0.80 (95% CI 0.79-0.81), 0.80 (95% CI 0.78-0.81), and 0.78 (95% CI 0.77-0.80) for the XGBoost, RF, SVM, and LR models, respectively. Weight satisfaction was the top predictor, and ethnicity, age, and gender were also consistent predictor variables of overweight or obesity status in all models. Based on multi-domain online workplace survey data, this study produced predictive models that identified overweight or obesity status with moderate to high accuracy. The performance of both ML-based and logistic regression models were comparable when predicting obesity among working adults in Malaysia.
No related grants have been discovered for Lei Hum Wee.