Dataset Information

Prevalence and predictors of road crash involvement among medical doctors in Malaysia: a cross-sectional study protocol.

ABSTRACT: INTRODUCTION:Medical doctors are often subjected to long working hours with minimal rest in between the shifts. This has led to many fatal and non-fatal road crash involvement (RCI). This study aims to determine the prevalence and predictors of RCI among medical doctors in Malaysia. METHODS AND ANALYSIS:This is a cross-sectional study among 375 Malaysian medical doctors who met the inclusion criteria. A predetermined self-administered questionnaires will be used to collect information regarding the sociodemographic, health status, workplace information, work commuting information, driving behaviour, history of RCI, fatigue, sleep quality, mental health status and work engagement. The questionnaires consist of the following instruments: (1) sociodemographic, health status, workplace information, work commuting information, driving behaviour and history of RCI; (2) Checklist of Individual Strength Questionnaire; (3) Pittsburgh Sleep Quality Index; (4) 21-item Depression Anxiety and Stress Scale; and (5) Utrecht's Work Engagement Scale. The data will be analysed using SPSS program V.24. Descriptive and inferential statistics will be used to determine the prevalence and predictors of RCI. ETHICS AND DISSEMINATION:This study protocol has received ethics approval from the Medical Research and Ethics Committee (MREC), Ministry of Health Malaysia (NMRR-18-3983-40609) and the Ethics Committee for Research Involving Human Subject, University Putra Malaysia (JKEUPM). Online written informed consent will be obtained from each study participant by the researchers. Results of the study will be disseminated through relevant journals and conferences. TRIAL REGISTRATION NUMBER:NCT04243291.

SUBMITTER: Rashid AA

PROVIDER: S-EPMC7333866 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Prevalence and predictors of road crash involvement among medical doctors in Malaysia: a cross-sectional study protocol.

Rashid Aneesa Abdul AA Devaraj Navin Kumar NK Mohd Yusof Halidah H Mustapha Fauzan F Wong Shaw Voon SV Ismail Ahmad Filza AF Ismail Khairil Idham KI Qureshi Ahmad Munir AM Nordin Rusli Bin RB

BMJ open 20200702 7

<h4>Introduction</h4>Medical doctors are often subjected to long working hours with minimal rest in between the shifts. This has led to many fatal and non-fatal road crash involvement (RCI). This study aims to determine the prevalence and predictors of RCI among medical doctors in Malaysia.<h4>Methods and analysis</h4>This is a cross-sectional study among 375 Malaysian medical doctors who met the inclusion criteria. A predetermined self-administered questionnaires will be used to collect informa ...[more]

PMID: 32616493

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Project description:Road crash fatality is a universal problem of the transportation system. A massive death toll caused annually due to road crash incidents, and among them, vulnerable road users (VRU) are endangered with high crash severity. This paper focuses on employing machine learning-based classification approaches for modelling injury severity of vulnerable road users-pedestrian, bicyclist, and motorcyclist. Specifically, this study aims to analyse critical features associated with different VRU groups-for pedestrian, bicyclist, motorcyclist and all VRU groups together. The critical factor of crash severity outcomes for these VRU groups is estimated in identifying the similarities and differences across different important features associated with different VRU groups. The crash data for the study is sourced from the state of Queensland in Australia for the years 2013 through 2019. The supervised machine learning algorithms considered for the empirical analysis includes the K-Nearest Neighbour (KNN), Support Vector Machine (SVM) and Random Forest (RF). In these models, 17 distinct road crash parameters are considered as input features to train models, which originate from road user characteristics, weather and environment, vehicle and driver condition, period, road characteristics and regions, traffic, and speed jurisdiction. These classification models are separately trained and tested for individual and unified VRU to assess crash severity levels. Afterwards, model performances are compared with each other to justify the best classifier where Random Forest classification models for all VRU modes are found to be comparatively robust in test accuracy: (motorcyclist: 72.30%, bicyclist: 64.45%, pedestrian: 67.23%, unified VRU: 68.57%). Based on the Random Forest model, the road crash features are ranked and compared according to their impact on crash severity classification. Furthermore, a model-based partial dependency of each road crash parameters on the severity levels is plotted and compared for each individual and unified VRU. This clarifies the tendency of road crash parameters to vary with different VRU crash severity. Based on the outcome of the comparative analysis, motorcyclists are found to be more likely exposed to higher crash severity, followed by pedestrians and bicyclists.

Project description:BackgroundMedication risk communication is essential to ensure the safe use of medicines. However, very few nations worldwide have established effective risk communication systems. To date, the effectiveness of risk communication among healthcare professionals in Malaysia has never been evaluated. Our study aimed to (i) evaluate doctors' and pharmacists' awareness of regulatory risk communication methods; (ii) identify factors predicting the usefulness of these methods; and (iii) compare respondents' preferences for risk communication to outline suggestions for enhancement.MethodsWe conducted a nationwide cross-sectional survey covering four commonly used risk communications, namely a national drug bulletin, safety alerts, Direct Healthcare Professional Communication letters (DHPCs), and educational materials. Multiple logistic regression analysis was used to determine the association between independent variables and the usefulness of risk communication. We performed qualitative analysis of free-text responses to gain insights on respondents' perspectives on risk communication.ResultsOf the 1146 responses received, 650 were from pharmacists (56.7%). Among the four methods surveyed, 71.5% of respondents were aware of educational materials, while awareness of the other three methods ranged from 20.7 to 53.9%. Pharmacists had higher awareness of all four methods compared to doctors. Private sector respondents were more aware of DHPCs compared to those from the public sector. The strongest predictors for finding risk communication useful were being a pharmacist [odds ratio (OR) = 18.2; 95% CI: 10.98-30.07; p < 0.001], having ≥30 years' work experience [OR = 4.9; 95% CI: 1.98-12.08; p < 0.001], and working in the pharmaceutical industry [OR = 4.6; 95% CI: 1.08-19.72; p = 0.039]. Both doctors and pharmacists preferred risk communication in the English-language and electronic format. However, other preferences differed between the professions and sectors. Analysis of free-text comments revealed five core themes to guide risk communication enhancement strategies.ConclusionsRisk communication awareness differed between public and private sector doctors and pharmacists depending on communication source. Integrating our findings with the theory of effective communication, we provide suggestions for developing strategic plans on enhancing risk communication. Public-private sector collaboration is key in ensuring risk communication effectiveness.

Dataset Information

Prevalence and predictors of road crash involvement among medical doctors in Malaysia: a cross-sectional study protocol.

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Prevalence and predictors of road crash involvement among medical doctors in Malaysia: a cross-sectional study protocol.

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