ABSTRACT
Introduction: The COVID-19 pandemic significantly impacted the global teaching and learning process (TnL). Unfortunately, to date, not many qualitative studies have been published specifically on the impact of COVID-19 on the Family Medicine course, particularly in Malaysia. Hence, this study aimed to explore the impact of COVID-19 pandemic on the teaching and learning experience of undergraduate students undergoing their Family Medicine course at a local university. Methods: A qualitative study using focused group discussion (FGD), was conducted among undergraduate students during the lockdown period. A semi-structured interview guide was used to interview 20 students, in their fifth undergraduate year, undertaking their fourth to fifth week of a total six-week course. They were selected using purposive snowball sampling method. There were four focus group discussions (FGD) with five students in a group. All interviews were audio-taped, transcribed verbatim, and the contents were analysed using the standard content analysis framework. Subsequently, thematic content analysis was conducted, and three major themes were produced. Results: The three major themes were (1) facilitators to learning, (2) barriers to learning, and (3) rooms for improvement. Conclusion: Although COVID-19 pandemic had significant impact on TnL of Family Medicine course among undergraduates, the students remained optimistic and proposed some improvement from their point of view. However, a creative, realistic, effective, and impactful way of TnL, particularly in clinical aspects should be developed and experimented. Technological progress and advancement should permit this idea to be achievable and implemented in near future. [ FROM AUTHOR] Copyright of Malaysian Journal of Medicine & Health Sciences is the property of Universiti Putra Malaysia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
Surface ozone is damaging to human health and crop yields. When evaluating global air pollution risk, gridded datasets with high accuracy are desired to reflect the local variations in air pollution concentrations. Here, a cluster‐enhanced ensemble machine learning method was used to develop a new 0.5‐degree monthly surface ozone data set during 2003–2019 by combining numerous informative variables. The overall accuracy of our data set is 91.5% (90.8% for space and 92.3% for time). Historically, populations in South Asia, North Africa and Middle‐East, and High‐income North America are exposed to the highest ozone concentrations. Globally, the population weighted ozone concentration in the peak season is 47.07 ppbv. Our results highlight that ozone pollution is intensifying in some regions, and implicate air quality management is crucial to secure human health from air pollution. Plain Language Summary: Surface ozone is one of the most hazardous air pollutants to human health and plants. However, estimation of global surface ozone is still limited. Here, by using state‐of‐the‐art machine learning techniques, we fuse satellite, chemical transport model outputs, atmospheric reanalyses, and emission data with surface observations to construct a full coverage and long‐time period surface ozone data set. We demonstrate that surface population weighted ozone concentration in North America and Europe has decreased from 2003 to 2019, while ozone pollution in East Asia has intensified during 2016–2019. We also show at least 37% of the world's population is exposed to ozone greater than the World Health Organization's interim target one of 50 ppbv (MDA8) in the peak season. Our results could help identify the key regions for improving global air quality and offers an insightful data set for human health assessments and air quality management. Key Points: A cluster‐enhanced ensemble machine learning method can predict global surface ozone with high accuracyPopulations in South Asia, North Africa and Middle‐East, and High‐income North America are exposed to the highest MDA8 during 2003–2019At least 37% of world's population is exposed to greater than 50 ppbv MDA8 in peak seasons [ FROM AUTHOR] Copyright of Geophysical Research Letters is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)