scholarly journals Linking Podcasts With Social Media to Promote Community Health and Medical Research: Feasibility Study (Preprint)

2018 ◽  
Author(s):  
Joyce Balls-Berry ◽  
Pamela Sinicrope ◽  
Miguel Valdez Soto ◽  
Tabetha Brockman ◽  
Martha Bock ◽  
...  
Keyword(s):  
Social Media ◽  
Community Health ◽  
Health Research ◽  
Local Community ◽  
Research Approach ◽  
Community Partner ◽  
Research Information ◽  
Health Related ◽  
Community Health Needs Assessment ◽  
Patient Engagement In Research

BACKGROUND Linking podcasts with social media is a strategy to promote and disseminate health and health research information to the community without constraints of time, weather, and geography. OBJECTIVE To describe the process of creating a podcast library and promoting it on social media as a strategy for disseminating health and biomedical research topics to the community. METHODS We used a community and patient engagement in research approach for developing a process to use podcasts for dissemination of health and health research information. We have reported the aspects of audience reach, impressions, and engagement on social media through the number of downloads, shares, and reactions posted on SoundCloud, Twitter, and Facebook, among others. RESULTS In collaboration with our local community partner, we produced 45 podcasts focused on topics selected from a community health needs assessment with input from health researchers. Episodes lasted about 22 minutes and presented health-related projects, community events, and community resources, with most featured guests from Olmsted County (24/45, 53%). Health research was the most frequently discussed topic. Between February 2016 and June 2017, episodes were played 1843 times on SoundCloud and reached 1702 users on our Facebook page. CONCLUSIONS This study demonstrated the process and feasibility of creating a content library of podcasts for disseminating health- and research-related information. Further examination is needed to determine the best methods to develop a sustainable social media plan that will further enhance dissemination (audience reach), knowledge acquisition, and communication of health topics.

scholarly journals Asset Mapping as a Tool for Identifying Resources in Community Health: A Methodological Overview

2019 ◽  
Vol 2 (1) ◽  
pp. 13-25
Author(s):  
Tanvir C Turin ◽  
Maaz Shahid ◽  
Marcua Vaska
Keyword(s):  
Community Health ◽  
Health Research ◽  
Community Partnerships ◽  
Research Community ◽  
Research Projects ◽  
Intended Audience ◽  
Asset Mapping ◽  
Surface Level ◽  
Health Related ◽  
The Creation

Background: By focusing on a community’s strengths instead of its’ weaknesses, the process of asset mapping provides researchers a new way to assess community health. This process is also a useful tool for assessing health-related needs, disparities, and inequities within the communities. This paper aims to serve as a basic and surface level guide to understanding and planning for creating an asset map. Methods: A step-by-step guideline is provided in this paper as an introduction to those interested in creating an asset map using organizational outlines and previous application in research projects. Results: To help readers better grasp asset maps, a few examples are first provided that show the application of asset maps in health research, community engagement, and community partnerships. This is followed by elaboration of the six steps involved in the creation of an asset map. Conclusion: This paper introduces researchers to the steps required to create an asset map, with examples from published literature. The intended audience includes students and researchers new to the creation of asset maps.

scholarly journals Automating Dynamic Consent Decisions for the Processing of Social Media Data in Health Research

2019 ◽  
Vol 15 (3) ◽  
pp. 187-201
Author(s):  
Chris Norval ◽  
Tristan Henderson
Keyword(s):  
Social Media ◽  
Health Research ◽  
User Study ◽  
Machine Learning Algorithms ◽  
Middle Ground ◽  
Social Media Data ◽  
Dynamic Consent ◽  
Significant Burden ◽  
Health Related ◽  
Media Data

Social media have become a rich source of data, particularly in health research. Yet, the use of such data raises significant ethical questions about the need for the informed consent of those being studied. Consent mechanisms, if even obtained, are typically broad and inflexible, or place a significant burden on the participant. Machine learning algorithms show much promise for facilitating a “middle-ground” approach: using trained models to predict and automate granular consent decisions. Such techniques, however, raise a myriad of follow-on ethical and technical considerations. In this article, we present an exploratory user study ( n = 67) in which we find that we can predict the appropriate flow of health-related social media data with reasonable accuracy, while minimizing undesired data leaks. We then attempt to deconstruct the findings of this study, identifying and discussing a number of real-world implications if such a technique were put into practice.

scholarly journals Cree Youth Engagement in Health Planning

2020 ◽  
Vol 15 (1) ◽  
pp. 73-89
Author(s):  
Nickoo Merati ◽  
Jonathan Salsberg ◽  
Joey Saganash ◽  
Joshua Iserhoff ◽  
Kaitlynn Hester Moses ◽  
...  
Keyword(s):  
Local Community ◽  
Health Planning ◽  
Community Based Participatory Research ◽  
Indigenous Communities ◽  
Research Approach ◽  
Youth Engagement ◽  
Youth Health ◽  
Indigenous Youth ◽  
Health Related ◽  
Use Of Social Media

Indigenous communities experience a greater burden of ill health than all other communities in Canada. Across the (Indigenous Region), all nine (Name) communities experience similar health challenges. In 2014, the (REGIONAL_BOARD) supported an initiative to stimulate local community prioritization for health change. While many challenges identified were specific to youth (10-29 years of age), youth’s perspectives in these reports to date have been limited. We sought to understand how (Indigenous) youth perceived youth health and their engagement in health and health planning across (Region). As part of a (REGIONAL_BOARD-University) partnership, this qualitative descriptive study adopted a community-based participatory research approach. Ten (Indigenous) youth participated in two focus groups, and five (Indigenous) youth coordinators participated in key informant interviews. Thematic analysis was conducted and inductive codes were grouped into themes. (Indigenous) participants characterized youth engagement into the following levels: participation in community and recreational activities; membership in youth councils at the local and regional levels; and, in decision-making as planners of health-related initiatives. (Indigenous) youth recommended greater use of social media, youth assemblies, and youth planners to strengthen their engagement and youth health in the region. Our findings revealed an interconnectedness between youth health and youth engagement; (Indigenous) youth described how they need to be engaged to be healthy, and need to be healthy to be engaged. (Indigenous) participants contributed novel and practical insights to engage Indigenous youth in health planning across Canada.

scholarly journals Challenges of Introducing Participant Observation to Community Health Research

ISRN Nursing ◽  
2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Meng Zhao ◽  
Yingchun Ji
Keyword(s):  
Community Health ◽  
Health Research ◽  
Research Study ◽  
Participant Observation ◽  
Health Care Research ◽  
Clinical Settings ◽  
Observation Data ◽  
Community Settings ◽  
Health Related ◽  
Observation Strategies

Participant observation elicits unique observation data from both an insider’s and an outsider’s perspectives. Despite the growing tendency to adopt participant observation strategies in health care research regarding health-related beliefs and types of behavior, the use of participant observation in current research is mostly limited to structured clinical settings rather than community settings. In this paper, we describe how we use participant observation in a community health research study with Chinese-born immigrant women. We document discrepancies between these women’s beliefs and types of behavior regarding health and health promotion. We further discuss the ethnical, time, and setting challenges in community health research using participant observation. Possible solutions are also discussed.

scholarly journals A Bibliometric Analysis of the Health Field Regarding Social Networks and Young People

2019 ◽  
Vol 16 (20) ◽  
pp. 4024 ◽  
Author(s):  
Pilar Aparicio-Martinez ◽  
Alberto-Jesus Perea-Moreno ◽  
María Pilar Martinez-Jimenez ◽  
María Dolores Redel-Macías ◽  
Manuel Vaquero-Abellan ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Young People ◽  
Health Research ◽  
Keyword Search ◽  
Public Health Research ◽  
Research Collaborations ◽  
Online Social Media ◽  
Health Related ◽  
History Of

Social networks have historically been used to share information and support regarding health-related topics, and this usage has increased with the rise of online social media. Young people are high users of social media, both as passive listeners and as active contributors. This study aimed to map the trends in publications focused on social networks, health, and young people over the last 40 years. Scopus and the program VOSviewer were used to map the frequency of the publications, keywords, and clusters of researchers active in the field internationally. A structured keyword search using the Scopus database yielded 11,966 publications. The results reveal a long history of research on social networks, health, and young people. Research articles were the most common type of publication (68%), most of which described quantitative studies (82%). The main discipline represented in this literature was medicine, with 6062 documents. North American researchers dominate the field, both as authors and partners in international research collaborations. The present article adds to the literature by elucidating the growing importance of social networks in health research as a topic of study. This may help to inform future investments in public health research and surveillance using these novel data sources.

scholarly journals Consumption of Health-Related Content on Social Media Among Adolescent Girls: Mixed-Methods Pilot Study (Preprint)

2018 ◽  
Author(s):  
Miriam P Leary ◽  
Emily N Clegg ◽  
Madison E Santella ◽  
Pamela J Murray ◽  
Julie S Downs ◽  
...  
Keyword(s):  
Social Media ◽  
Mixed Methods ◽  
Health Information ◽  
Adolescent Girls ◽  
Online Survey ◽  
Local Community ◽  
School Age ◽  
Attitudes And Practices ◽  
Related Information ◽  
Health Related

BACKGROUND Consumption of health- and fitness-related social media content is a predominant behavior among teenage girls, which puts them at risk for consuming unreliable health-related information. OBJECTIVE This mixed-methods study (qualitative and quantitative) assessed health behavior attitudes and practices as well as social media use among adolescent girls. Additionally, similar practices and behaviors of adults who regularly interact with this population were studied. METHODS Girls aged 12-18 years were recruited to complete a 28-item survey and participate in a 45- to 60-minute focus group. Adults who regularly interact with adolescent girls, including parents, teachers, and healthcare professionals, were recruited from the local community and given a link to provide online consent and complete a survey. RESULTS A total of 27 adolescent girls participated in one of nine focus groups. Participants included 18 high school (age: mean 16.1 years; SD 1.3 years) and 9 middle school (age: mean 12.4 years; SD 0.7 years) girls. Eleven adults completed the online survey. Adolescents used social media to communicate and connect with friends, rather than as a source of health information. Although adolescents may see health-related content, most do not follow health-related pages or share such pages themselves, and fewer are actively searching for this information. Adolescents tend to trust information from familiar sources, and the participants reported that they do not follow official news accounts. Adults considered modeling and discussing healthy behaviors important and reportedly expected adolescents to see some level of health-related, especially fitness-related, content on social media. CONCLUSIONS Education interventions are warranted for both adolescents and adults with whom adolescent girls regularly interact, in the areas of sedentary behavior to guide them to access reliable online health-related information and be judicious consumers of online health information.

P051 USING A HUMAN-CENTERED QUALITATIVE RESEARCH APPROACH FOR DEVELOPING IBD EDUCATIONAL VIDEOS DELIVERED THROUGH SOCIAL MEDIA

2020 ◽  
Vol 158 (3) ◽  
pp. S108-S109
Author(s):  
Carine Khalil ◽  
Welmoed van Deen ◽  
Taylor Dupuy ◽  
Nirupama Bonthala ◽  
Christopher Almario ◽  
...  
Keyword(s):  
Qualitative Research ◽  
Social Media ◽  
Research Approach ◽  
Educational Videos

Trend of Parental Refusal or Delay of Childhood Vaccinations

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Meagan Marie Daoust
Keyword(s):  
Public Health ◽  
Social Media ◽  
Healthcare System ◽  
Educational Context ◽  
Health Consequences ◽  
Missed Opportunities ◽  
The Public ◽  
Health Related ◽  
Parental Refusal

The healthcare trend of parental refusal or delay of childhood vaccinations will be investigated through a complex Cynefin Framework component in an economic and educational context, allowing patterns to emerge that suggest recommendations of change for the RN role and healthcare system. As a major contributing factor adding complexity to this trend, social media is heavily used for health related knowledge, making it is difficult to determine which information is most trustworthy. Missed opportunities for immunization can result, leading to economic and health consequences for the healthcare system and population. Through analysis of the powerful impact social media has on this evolving trend and public health, an upstream recommendation for RNs to respond with is to utilize reliable social media to the parents’ advantage within practice. The healthcare system should focus on incorporating vaccine-related education into existing programs and classes offered to parents, and implementing new vaccine classes for the public.

Measuring Relevant Information in Health Social Network Conversations and Clinical Diagnosis Cases (Preprint)

2018 ◽  
Author(s):  
Albert Moreira ◽  
Raul Alonso-Calvo ◽  
Alberto Muñoz ◽  
Jose Crespo
Keyword(s):  
Social Media ◽  
Social Network ◽  
Relevant Information ◽  
Online Discussions ◽  
Biomedical Knowledge ◽  
Expert Opinions ◽  
Health Related ◽  
Health Social Network ◽  
Discussion System ◽  
Clinical Cases

BACKGROUND Internet and Social media is an enormous source of information. Health Social Networks and online collaborative environments enable users to create shared content that afterwards can be discussed. While social media discussions for health related matters constitute a potential source of knowledge, characterizing the relevance of participations from different users is a challenging task. OBJECTIVE The aim of this paper is to present a methodology designed for quantifying relevant information provided by different participants in clinical online discussions. METHODS A set of key indicators for different aspects of clinical conversations and specific clinical contributions within a discussion have been defined. These indicators make use of biomedical knowledge extraction based on standard terminologies and ontologies. These indicators allow measuring the relevance of information of each participant of the clinical conversation. RESULTS Proposed indicators have been applied to two discussions extracted from PatientsLikeMe, as well as to two real clinical cases from the Sanar collaborative discussion system. Results obtained from indicators in the tested cases have been compared with clinical expert opinions to check indicators validity. CONCLUSIONS The methodology has been successfully used for describing participant interactions in real clinical cases belonging to a collaborative clinical case discussion tool and from a conversation from a Health Social Network.

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

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