The purpose of this study was to examine factors important to older adults who agreed with a deprescribing recommendation given by a general practitioner (GP) to a hypothetical patient experiencing polypharmacy. We conducted an online, vignette‐based, experimental study in the United Kingdom, United States and Australia with participants ≥65 years. The primary outcome was an agreement with a deprescribing recommendation (6‐point Likert scale; 1 = strongly disagree and 6 = strongly agree). We performed a content analysis of the free‐text reasons provided by participants who agreed with deprescribing (score of 5 or 6). Among 2656 participants who agreed with deprescribing, approximately 53.7% shared a preference for following the GP's recommendation or considered the GP the expert. The medication was referred to as a reason for deprescribing by 35.6% of participants. Less common themes included personal experience with medicine (4.3%) and older age (4.0%). Older adults who agreed with deprescribing in a hypothetical vignette most frequently reported a desire to follow the recommendations given the GP's expertise. Future research should be conducted to help clinicians efficiently identify patients who have a strong desire to follow the doctor's recommendations related to deprescribing, as this may allow for a tailored, brief deprescribing conversation.
In the past few months we all have been affected by Covid-19 pandemic. From March 2020 schools and colleges were closed due to increased cases of covid-19 across India. We can say this was one of the biggest pandemic of the century. We all remain closed in our homes with proper distancing in this pandemic lot of things in our habit, lifestyle and behaviour has been changed. In this pandemic our Education system completely relies on online/ICT based teaching and learning because besides there is no alternative left for us. In the phase of ICT based online teaching and learning specifically on school level students and teachers suffer a lot of problems to adapt new techniques on such a vast level because in School level for every active classroom in transition of knowledge pedagogy takes an important role. But in online mode face to face teaching is not possible for us. And in a sudden shift towards online teaching, online assessment is also a big concern for school, teachers and government. In online mode assessment going through many challenges. In this research paper researchers address the issue related to online assessment from the point of views of students, parents, teachers, school management and policy makers because assessment is the thing which leads students towards improvement and excellence.
In our modern society where the internet is ubiquitous, everyone relies on various online resources for news. Along with the internet in the use of social media platforms like Facebook, Dataset, WhatsApp etc. News spread rapidly among millions of users within a very short span of time. The spread of fake news has far-reaching consequences like the creation of based opinions to swaying election outcomes for the benefit of certain candidates. Moreover, spammers use appealing news headlines to generate revenue using advertisement via click- baits. In this project we aim to perform classification of various news article available online with the help of concepts of Machine Learning. We aim to provide the user with the ability to classify the news which is fake or real with the help of some algorithm used in Machine Learning. This work purposes the use of machine learning techniques to detect Fake news. In the experiments used: Support Vector Machine (SVM). The normalization method is important step for cleansing data before using the machine learning method to classify data. We are aiming Support Vector Machine result should reach the higher accuracy level. Besides of machine learning we are using HTML, Python each one of has it's individual purpose for successful creation to build fake news detection KEYWORDS: Internet, Social Media, Fake News, Classification, Artificial Intelligence, Machine Learning, Websites, Python, HTML.
Objectives To explore the extent to which adults 65-years and older who reported taking 10 or more non-prescription products were interested in deprescribing. Methods During an online semi-structured interview, participants were asked to imagine their primary care provider raised the idea of deprescribing. Participants sorted each prescription and non-prescription medication into a category: continue, stop or lower (deprescribe), or unsure. Findings were summarized using descriptive statistics and thematic analysis. Key findings Participants (n = 15) were interested in deprescribing 6% of the non-prescription medications (n = 12/207). Conclusions Older adults were resistant to deprescribing non-prescription products.
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