The Effects of Website Traits and Medical Skepticism on Patients’ Willingness to Follow Online Medical Advice (Preprint)

2021 ◽  
Author(s):  
Jennifer Claggett ◽  
Brent Kitchens ◽  
Maria Paino ◽  
Kaitlyn A. Beisecker Levin
Keyword(s):  
Medical Information ◽  
The United States ◽  
Medical Advice ◽  
Average Score ◽  
P Value ◽  
Discussion Forums ◽  
Online Discussion Forums ◽  
Internet Users ◽  
Online Sources ◽  
Medical Situation

BACKGROUND As people increasingly turn to online sources for medical information, we offer some insight into what website traits influence patient’s credibility assessment. Specifically, we control for brand and content length, while manipulating three website traits: (1) authorship, (2) format, and (3) tone. Further, we focus on medical skepticism to understand how patients with high levels of medical skepticism may react to online medical information differently. Medical skepticism is related to a patient’s doubts about the value of conventional medical care, and therefore skeptics may have different practices and criteria when conducting their own online medical searches. OBJECTIVE This study evaluates how website traits impact the likelihood that patients follow online medical advice and how this varies in patients with differing levels of medical skepticism. METHODS This experiment presented participants with a hypothetical medical situation about leg cramps and offered a website with treatment advice. We varied the websites the participants observed across three traits: (1) authorship: patient or physician, (2) format: article or discussion forum, (3) tone: objective or experience-based. The 2,305 participants were randomly assigned one of eight possible conditions and then asked the extent to which they would follow the advice. Healthcare patterns and coverage, demographics, and their level of medical skepticism were captured. RESULTS Our panel data was selected to be demographically representative of the population of internet users in the United States. The 2,305 complete responses were analyzed with OLS regression. Our analysis reveals that people are more likely to accept online medical advice authored by a physician (P-value<.001) and presented with an objective tone (P-value<.001), but those preferences erode as levels of medical skepticism increase. Medical skepticism was measured via a previously established index on a 0-4 scale, and the average score was 2.26 with a standard deviation of 0.84. Individuals with higher levels of medical skepticism were more likely to follow online medical advice in our experiment (P-value<.001). Individuals with low levels of medical skepticism found online discussion forums more credible, while those with high levels of medical skepticism preferred articles (P-value<.01). We discuss the interactions between medical skepticism and all three website traits manipulated in the experiment. CONCLUSIONS Our findings suggest that, generally, physician authorship and an objective tone create more persuasive online medical advice. But, there are differences in how patients with high levels of medical skepticism react to online medical resources. Medical skeptics are less discerning regarding the author’s credentials and the presentation tone of the information. Further, those with higher levels of medical skepticism prefer article format presentations, whereas those with lower levels prefer forum-style formatting.

scholarly journals Online monthly discussion module of simulation-based teaching: Effect on knowledge

2021 ◽  
Vol 8 (2) ◽  
pp. 48-52
Author(s):  
Niket Verma ◽  
Maria Thomas ◽  
Dinesh K Badyal
Keyword(s):  
Medical Education ◽  
Online Discussions ◽  
Average Score ◽  
P Value ◽  
Discussion Forums ◽  
Medical College ◽  
Online Discussion Forums ◽  
Higher Level Thinking ◽  
Simulation Based ◽  
Knowledge Levels

Online discussion forums engage learners in higher-level thinking, allowing them to explore topics in much greater depth. One such formal online professional discussion platform is the two-year Foundation for Advancement of International Medical Education and Research (FAIMER) Fellowship offered by the Christian Medical College Ludhiana (CMCL) - FAIMER Regional Institute (CMCL-FRI). In this study, we report the results of a survey conducted among FAIMER fellows after attending online discussions on Simulation-based teaching (SBT) to evaluate their change in knowledge levels on the topic. This was a retrospective analysis of pre-moderation and post-moderation questionnaire responses. The questions/statements were designed to cover the entire range of topics planned to be discussed during the moderation month.: While the median score between the pre-moderation and post-moderation month questionnaires remained the same, the average score showed an increase from 9.5 to 10.37. The number of fellows who scored the maximum possible score of 12 showed a significant increase from 2.94% to 23.33% between the pre-session to the post-moderation month questionnaires (p-value=0.015). The percentage of respondents who answered the questions correctly in the post-moderation month questionnaire showed an increase over the pre-moderation month questionnaire in 10 out of 12 questions, with the increase being highly significant in 2 out of these 10 questions. Attending online ML web discussions leads to an increase in knowledge levels among participants and is an effective way to introduce medical educationists to essential concepts in medical education.

A Global Conversation on Effective Technology Integration in Education

2012 ◽  
pp. 205-218
Author(s):  
Kay Kyeongju Seo ◽  
Aimee deNoyelles
Keyword(s):  
United Arab Emirates ◽  
United States Of America ◽  
Online Discussion ◽  
The United States ◽  
Cultural Barriers ◽  
Discussion Forums ◽  
Online Discussion Forums ◽  
The Republic ◽  
Online Conversation ◽  
Technology Perceptions

This chapter explored the technology perceptions and preparedness of pre-service and in-service teachers from three different countries. Twenty-one students in the Republic of Korea, twelve students in the United Arab Emirates, and thirty students in the United States of America were virtually connected. They participated in weekly online discussion forums for six weeks and shared how well prepared they felt about using technology in their content areas and how they would effectively use technology in their future classrooms. This study can serve as a good model for facilitating a global conversation and supporting a reflective online conversation across geographic distances and cultural barriers.

scholarly journals Students' Impressions on the Effectiveness of Online Discussion Forums

2018 ◽  
Vol 3 (1) ◽  
pp. 86-108
Author(s):  
Beena Vijayavalsalan
Keyword(s):  
Online Discussion ◽  
Learning Experience ◽  
Statistical Significance ◽  
Strong Association ◽  
Thinking Skills ◽  
P Value ◽  
Discussion Forums ◽  
Online Forums ◽  
Online Discussion Forums ◽  
Significant Difference

Purpose: The study has evaluated the effectiveness of online discussion forums among the students of Abu Dhabi University, UAE. Results: The results have shown strong association between positive learning experience and frequency of student participation in discussion forums. However, no statistical significance (p-value=0.306) has been observed among online forums and age of the participants for developing critical thinking skills. Moreover, a significant difference has been identified in students' participation on the effectiveness of online discussion (p-value=0.000). Conclusion: It is concluded that online discussion forums serve as an efficient and effective tool for interaction among the participants.

LATENT-SEMANTIC ANALYSIS, SOCIAL NETWORKS AND NON-STRUCTURED DATA: INTERACTION METHOD

2019 ◽  
Keyword(s):  
Social Networks ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Statistical Processing ◽  
Unstructured Data ◽  
Average Score ◽  
Text Type ◽  
Internet Users ◽  
The Matrix ◽  
Human Thinking

This article examines the method of latent-semantic analysis, its advantages, disadvantages, and the possibility of further transformation for use in arrays of unstructured data, which make up most of the information that Internet users deal with. To extract context-dependent word meanings through the statistical processing of large sets of textual data, an LSA method is used, based on operations with numeric matrices of the word-text type, the rows of which correspond to words, and the columns of text units to texts. The integration of words into themes and the representation of text units in the theme space is accomplished by applying one of the matrix expansions to the matrix data: singular decomposition or factorization of nonnegative matrices. The results of LSA studies have shown that the content of the similarity of words and text is obtained in such a way that the results obtained closely coincide with human thinking. Based on the methods described above, the author has developed and proposed a new way of finding semantic links between unstructured data, namely, information on social networks. The method is based on latent-semantic and frequency analyzes and involves processing the search result received, splitting each remaining text (post) into separate words, each of which takes the round in n words right and left, counting the number of occurrences of each term, working with a pre-created semantic resource (dictionary, ontology, RDF schema, ...). The developed method and algorithm have been tested on six well-known social networks, the interaction of which occurs through the ARI of the respective social networks. The average score for author's results exceeded that of their own social network search. The results obtained in the course of this dissertation can be used in the development of recommendation, search and other systems related to the search, rubrication and filtering of information.

Digging for the truth: the case for active annotation in evaluating the credibility of online medical information (Preprint)

2020 ◽  
Author(s):  
Mikołaj Morzy ◽  
Bartłomiej Balcerzak ◽  
Adam Wierzbicki ◽  
Adam Wierzbicki
Keyword(s):  
Machine Learning ◽  
Medical Information ◽  
Representation Learning ◽  
Training Dataset ◽  
Highly Qualified ◽  
Human In The Loop ◽  
Annotation Process ◽  
Comprehensive Framework ◽  
Online Sources ◽  
The Web

BACKGROUND With the rapidly accelerating spread of dissemination of false medical information on the Web, the task of establishing the credibility of online sources of medical information becomes a pressing necessity. The sheer number of websites offering questionable medical information presented as reliable and actionable suggestions with possibly harmful effects poses an additional requirement for potential solutions, as they have to scale to the size of the problem. Machine learning is one such solution which, when properly deployed, can be an effective tool in fighting medical disinformation on the Web. OBJECTIVE We present a comprehensive framework for designing and curating of machine learning training datasets for online medical information credibility assessment. We show how the annotation process should be constructed and what pitfalls should be avoided. Our main objective is to provide researchers from medical and computer science communities with guidelines on how to construct datasets for machine learning models for various areas of medical information wars. METHODS The key component of our approach is the active annotation process. We begin by outlining the annotation protocol for the curation of high-quality training dataset, which then can be augmented and rapidly extended by employing the human-in-the-loop paradigm to machine learning training. To circumvent the cold start problem of insufficient gold standard annotations, we propose a pre-processing pipeline consisting of representation learning, clustering, and re-ranking of sentences for the acceleration of the training process and the optimization of human resources involved in the annotation. RESULTS We collect over 10 000 annotations of sentences related to selected subjects (psychiatry, cholesterol, autism, antibiotics, vaccines, steroids, birth methods, food allergy testing) for less than $7 000 employing 9 highly qualified annotators (certified medical professionals) and we release this dataset to the general public. We develop an active annotation framework for more efficient annotation of non-credible medical statements. The results of the qualitative analysis support our claims of the efficacy of the presented method. CONCLUSIONS A set of very diverse incentives is driving the widespread dissemination of medical disinformation on the Web. An effective strategy of countering this spread is to use machine learning for automatically establishing the credibility of online medical information. This, however, requires a thoughtful design of the training pipeline. In this paper we present a comprehensive framework of active annotation. In addition, we publish a large curated dataset of medical statements labelled as credible, non-credible, or neutral.

scholarly journals Integrating Peer Learning Activities and Problem-Based Learning in Clinical Nursing Education

2021 ◽  
Vol 7 ◽  
pp. 237796082110002
Author(s):  
Suci Tuty Putri ◽  
Sri Sumartini
Keyword(s):  
Nursing Education ◽  
Problem Based Learning ◽  
Sampling Technique ◽  
Peer Learning ◽  
Average Score ◽  
Control Group ◽  
P Value ◽  
Clinical Learning ◽  
Control Group Design ◽  
Learning Achievement

Introduction The implementation of nursing clinical learning in Indonesia has several challenges that require innovation in the learning method strategy. The method that has been used so far focuses on the hierarchical relationship between lecturers/preceptors and students, so that there are many shortcomings in learning outcomes. The application of the method of active learning with Peer Learning (PL) and Problem Based Learning (PBL) techniques has proven effective in classroom learning, but its rarely found in clinical learning. Objective The purpose of this study was to determine the effectiveness of the PL and PBL towards the achievement of clinical learning in nursing students. Methods The research method used a true experiment with a posttest only control group design, the sampling technique was taken by randomize control trial. An instrument for clinical learning achievement using AssCE. Results The analysis was carried out as descriptive and bivariate. The results showed the mean in the experimental group was 7.059 and the control group was 6.325. Further statistical test results were obtained p-value = 0.001 (p < 0.05) which showed that the average score there were differences in clinical learning achievement development scores. Conclusion Clinical learning using peer learning and PBL methods can directly improve various aspects of student competency achievement.

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