METHOD: An explanatory mixed method research design was carried out on first year medical students at a private university in Malaysia. In Phase I, a survey was conducted to explore the effectiveness of jigsaw learning. Descriptive and inferential statistics were calculated using SPSS. In Phase II, a focus group interview was conducted to explore their in-depth experiences. Qualitative data were thematically analysed.
RESULTS: Fifty-seven students participated in the survey and seven students took part in the focus group interview. Quantitative data analysis showed a statistically significant improvement in the student's individual accountability, promotive interaction, positive interdependence, interpersonal skill, communication skill, teamwork skill, critical thinking and consensus building after jigsaw learning sessions. Qualitative data explained their experiences in-depth.
CONCLUSION: Jigsaw cooperative learning improves collaboration, communication, cooperation and critical thinking among the undergraduate medical students. Educators should use jigsaw learning methods to encourage effective collaboration and team working. Future studies should explore the effectiveness of the jigsaw cooperative learning technique in promoting interprofessional collaboration in the workplace.
METHODS: Various combinations of keywords related to "digital health", "intervention", "workplace" and "developing country" were applied in Ovid MEDLINE, EMBASE, CINAHL Plus, PsycINFO, Scopus and Cochrane Library for peer-reviewed articles in English language. Manual searches were performed to supplement the database search. The screening process was conducted in two phases and a narrative synthesis to summarise the data. The review protocol was written prior to undertaking the review (OSF Registry:10.17605/OSF.IO/QPR9J).
RESULTS: The search strategy identified 10,298 publications, of which 24 were included. Included studies employed the following study designs: randomized-controlled trials (RCTs) (n = 12), quasi-experimental (n = 4), pilot studies (n = 4), pre-post studies (n = 2) and cohort studies (n = 2). Most of the studies reported positive feedback of the use of digital wellness interventions in workplace settings.
CONCLUSIONS: This review is the first to map and describe the impact of digital wellness interventions in the workplace in LMICs. Only a small number of studies met the inclusion criteria. Modest evidence was found that digital workplace wellness interventions were feasible, cost-effective, and acceptable. However, long-term, and consistent effects were not found, and further studies are needed to provide more evidence. This scoping review identified multiple digital health interventions in LMIC workplace settings and highlighted a few important research gaps.
OBJECTIVES: This paper discusses activity detection and analysis (ADA) using security robots in workplaces. The application scenario of this method relies on processing image and sensor data for event and activity detection. The events that are detected are classified for its abnormality based on the analysis performed using the sensor and image data operated using a convolution neural network. This method aims to improve the accuracy of detection by mitigating the deviations that are classified in different levels of the convolution process.
RESULTS: The differences are identified based on independent data correlation and information processing. The performance of the proposed method is verified for the three human activities, such as standing, walking, and running, as detected using the images and sensor dataset.
CONCLUSION: The results are compared with the existing method for metrics accuracy, classification time, and recall.