METHOD: This study presents the publication trends, research landscape, and scientific developments related to safety management systems (SMS) based on published documents from the Elsevier Scopus database. Published documents on SMS and indexed in Scopus are identified, screened, and analyzed to examine the publication trends, research developments, and scientific landscape. For this purpose, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), bibliometric analysis (B.A.), and systematic literature review (SLR) procedures are used. The results reveal that 799 related documents were published between 2001 and 2021.
RESULTS: The most productive stakeholders, that is, top researchers, affiliations, and countries, include Liesbeth Jacxsens, Universiteit Gent (Belgium), and the United States. This study shows that the availability of research grants, incentives, or awards is critical to the productivity of top researchers, institutions, and nations actively researching SMS topics. The bibliometric analysis reveals that the topic is characterized by high productivity, co-authorships, keyword occurrence, and citations.
CONCLUSION: The analysis shows that SMS research is a broad, multidimensional, and impactful area that has become essential for identifying, reducing, monitoring, and eliminating risks in many industries. It is concluded that the topic of the SMS remains relevant because of its impact on human health, occupational safety, and environmental well-being.
PRACTICAL APPLICATIONS: This study provides in-depth insight into expanding the scope of SMS research. Moreover, research and policymakers can facilitate decision-making and collaboration based on this study's outtakes.
AIMS: This study aims to develop and evaluate the effectiveness of the safety and health programme TRIMOSH (Theory-Based Intervention Module on Occupational Safety and Health) in improving the knowledge, attitude, and practice among food industry workers.
METHODS: TRIMOSH intervention study is a two-arm randomised, single-blinded, controlled, parallel trial that will be conducted among food industry workers in Selangor, Malaysia. In a partnership with Food Handler Training Schools in Selangor, 10 pairs of Food Handler Training Schools with 12 participants per group (n = 240) will be recruited for balanced randomisation intervention and control conditions. Furthermore, data collection of all participants was conducted at four time points: baseline (T0), immediately (T1), one month (T2), and three months (T3) post-intervention. Generalised Linear Mixed Model (GLMM) will be conducted to determine the effects of intervention within and between study groups. Subsequently, the primary outcomes increase the knowledge, attitude, and practice (KAP) of safety and health at food premises. Clinical Trial Registry registration was approved by the ClinicalTrials.gov committee on October 2022 with the ClinicalTrials.gov Identifier: NCT05571995. This study has also been approved by the Ethics Committee for Research Involving Human Subjects of Universiti Putra Malaysia (JKEUPM-2022-346). All participants are required to provide consent prior to participation.
CONCLUSIONS: The characteristics of the respondents are expected to show no difference between the groups. It is hypothesised that TRIMOSH is effective in improving the knowledge, attitude, and practices of food industry workers in Selangor. The results will be reported and presented in international peer-reviewed journals, conferences, and other platforms. In addition, the TRIMOSH programme will be offered at the national level by the relevant authorities for the benefit of food industry workers.
METHODS: This study used PubMed and Scopus databases to conduct a systematic literature search using a set of keywords. The selected records dated from 1 January 2016 to 8 September 2021 were extracted into the Mendeley Desktop and ATLAS.ti 8 software. Systematic screening was conducted by two independent researchers and finalized by the third researcher. Data were coded and grouped according to the themes. The results were presented as the table for descriptive analysis and cross-tabulation between the themes.
RESULTS: A total of 120 records were included in this study. Under the theme of main health problems, the findings showed that mental health, infectious disease, and work-related musculoskeletal disorders are the top three problems being discussed in the literature for the working people in Malaysia. The findings also showed an increasing trend of mental health problems during pandemic COVID-19 years. In addition, hospital was the highest workplace where the occupational health problems were reported.Discussion/Conclusion. There was substantial work on the mental health problem, infectious diseases, and work-related musculoskeletal disorders as the main health problem among workers in Malaysia in the past five years. The employers must report any occupational health and injury case to the authority and prompt intervention can be initiated.
METHOD: Based on the proposed model, a quantitative method was employed to obtain data from G7 construction industry operating within the peninsular Malaysia. Out of the 180 copies of questionnaire, 165 copies were properly filled, returned, and used for the analysis. PLS-SEM was used to analyze the obtained data.
RESULTS: The findings of the study affirmed that specialization, centralization, and management of risk by the construction industry had positive correlation.
CONCLUSIONS: As anticipated, coercive pressure had positive moderating correlation with both formalization and the management of risk by the construction industry. Similarly, it was also found that in the course of carrying out construction activities, coercive pressure made significant interactive influence on formalization, specialization, and centralization. Practical Applications: Coercive pressure reduced the frequency of accidents among workers in the process of carrying out construction works.
OBJECTIVES: In this paper, the Advanced Human-Robot Collaboration Model (AHRCM) approach is to enhance the risk assessment and to make the workplace involving security robots. The robots use perception cameras and generate scene diagrams for semantic depictions of their environment. Furthermore, Artificial Intelligence (AI) and Information and Communication Technology (ICT) have utilized to develop a highly protected security robot based risk management system in the workplace.
RESULTS: The experimental results show that the proposed AHRCM method achieves high performance in human-robot mutual adaption and reduce the risk.
CONCLUSION: Through an experiment in the field of human subjects, demonstrated that policies based on the proposed model improved the efficiency of the human-robot team significantly compared with policies assuming complete human-robot adaptation.