Objective: To analyze the video sources, contents and quality of YouTube videos about the topic of medical professionalism.
Methods: A systematic search was accomplished on YouTube videos during the period between March 1, 2020 and March 27, 2020. The phrases as significant words used throughout YouTube web search were 'Professionalism in Medical Education', Professionalism in medicine', 'Professionalism of medical students', 'Professionalism in healthcare'. 'Teaching professionalism', 'Attributes of professionalism'. The basic information collected for each video included author's/publisher's name, total number of watchers, likes, dislikes and positive and undesirable remarks. The videos were categorized into educationally useful and useless established on the content, correctness of the knowledge and the advices. Different variables were measured and correlated for the data analysis.YouTube website was searched the using keywords 'Professionalism in Medical Education', Professionalism in medicine', 'Professionalism of medical students', 'Professionalism in healthcare'. 'Teaching professionalism', and 'Attributes of professionalism'.
Results: After 2 rounds of screening by the subject experts and critical analysis of all the 137 YouTube videos, only 41 (29.92%) were identified as pertinent to the subject matter, i.e., educational type. After on expert viewing these 41 videos established upon our pre-set inclusion/exclusion criteria, only 17 (41.46%) videos were found to be academically valuable in nature.
Conclusion: Medical professionalism multimedia videos uploaded by the healthcare specialists or organizations on YouTube provided reliable information for medical students, healthcare workers and other professional. We conclude that YouTube is a leading and free online source of videos meant for students or other healthcare workers yet the viewers need to be aware of the source prior to using it for training learning.
Methods: An online questionnaire survey method was used. Based on sample size calculation, a total of 1,508 UiTM staff and students from ten selected campuses of Universiti Teknologi MARA (UiTM) were invited to participate in this survey. An up-to-date e-mail list of staff in the selected campuses was used as the sampling frame for the study, whereas the students were recruited from the official university student Facebook portal.
Results: A total of 788 respondents participated in this survey, 72.2% of them knew about facial candling, though only 35.4% had tried the treatment. Approximately one-fifth of respondents agreed that facial candling might treat AR. It was found that a higher number of users than nonusers agreed that facial candling was a traditional medicine (78.9% vs 55.0%); could be used on the face and ears (83.5% vs 45.4%); and could be self-administered at home (83.5 vs 45.4%). Interestingly, more than half of them were uncertain about its long-term effects and adverse reactions.
Conclusion: This study confirms the facial candling use among patients with AR although the percentage is low. The patients and general public need to be better informed about the use of facial candling in AR and its associated risks.
METHODS: A systematic literature search for studies with the primary aim of using OSN to detect and track a pandemic was conducted. We conducted an electronic literature search for eligible English articles published between 2004 and 2015 using PUBMED, IEEExplore, ACM Digital Library, Google Scholar, and Web of Science. First, the articles were screened on the basis of titles and abstracts. Second, the full texts were reviewed. All included studies were subjected to quality assessment.
RESULT: OSNs have rich information that can be utilized to develop an almost real-time pandemic surveillance system. The outcomes of OSN surveillance systems have demonstrated high correlations with the findings of official surveillance systems. However, the limitation in using OSN to track pandemic is in collecting representative data with sufficient population coverage. This challenge is related to the characteristics of OSN data. The data are dynamic, large-sized, and unstructured, thus requiring advanced algorithms and computational linguistics.
CONCLUSIONS: OSN data contain significant information that can be used to track a pandemic. Different from traditional surveys and clinical reports, in which the data collection process is time consuming at costly rates, OSN data can be collected almost in real time at a cheaper cost. Additionally, the geographical and temporal information can provide exploratory analysis of spatiotemporal dynamics of infectious disease spread. However, on one hand, an OSN-based surveillance system requires comprehensive adoption, enhanced geographical identification system, and advanced algorithms and computational linguistics to eliminate its limitations and challenges. On the other hand, OSN is probably to never replace traditional surveillance, but it can offer complementary data that can work best when integrated with traditional data.
OBJECTIVE: In an attempt to understand this relationship, this study aimed to carry out an investigation on online intervention features for effective management of Facebook addiction in higher education.
METHODS: This study was conducted quantitatively using surveys and partial least square-structural equational modeling. The study involved 200 postgraduates in a Facebook support group for postgraduates. The Bergen Facebook Addiction test was used to assess postgraduates' Facebook addiction level, whereas online intervention features were used to assess postgraduates' perceptions of online intervention features for Facebook addiction, which are as follows: (1) self-monitoring features, (2) manual control features, (3) notification features, (4) automatic control features, and (5) reward features.
RESULTS: The study discovered six Facebook addiction factors (relapse, conflict, salience, tolerance, withdrawal, and mood modification) and five intervention features (notification, auto-control, reward, manual control, and self-monitoring) that could be used in the management of Facebook addiction in postgraduate education. The study also revealed that relapse is the most important factor and mood modification is the least important factor. Furthermore, findings indicated that notification was the most important intervention feature, whereas self-monitoring was the least important feature.
CONCLUSIONS: The study's findings (addiction factors and intervention features) could assist future developers and educators in the development of online intervention tools for Facebook addiction management in postgraduate education.