Increasing Awareness of Osteoporosis Between Saudi Women Using a Risk Prediction Tool via Social Media Platform (Preprint)

2021 ◽  
Author(s):  
Ala Maha Ali Lashin; Msaad Altelhi
Keyword(s):  
Science Research ◽  
Evidence Base ◽  
Medical Doctor ◽  
Prediction Algorithm ◽  
Prediction Tool ◽  
Saudi Women ◽  
Disease Awareness ◽  
Institutional Review ◽  
User Data ◽  
Media Platform

BACKGROUND Osteoporosis (OS) is a disease that affects many women in the country of Saudi Arabia. It can be detected and treated with proper intervention. Most studies' predictor analysis of current health conditions in regards to bone requires patients’ comprehensive data. Burdening patients with cost and exposing them to more medical procedures could be prevented with proactive intervention. Simple prediction, easy, and accessible tool needed for increasing women’s awareness of osteoporosis. OBJECTIVE The main aims of the presented work are increasing Saudi women's awareness of osteoporosis and improving their daily habits for preventing OS. For the first purpose, a tool was designed, developed, and evaluated as a prediction tool called OSPreKnow (Osteoporosis Pre-Know). OSPreKnow was designed to give an estimate of the possibility to have OS with current user data entered. The new concept of self-profiling is based on a programmed prediction algorithm in OS. METHODS The utility of OSPreKnow evaluated with designed science research. In our paper, the efficacy of OSPreKnow is explained for elevating knowledge about OS. A prediction tool is designed and implemented to gather input from 992 women. Knowledge and perception were measured to capture the tool’s impact. RESULTS Out of 992 who used the tool, 646 completed the post-tool survey and exhibited a significant satisfaction with the tool (69%) and level of knowledge after using the tool (74%). 480 reported more knowledge about OS which is 74% of respondents. This provided evidence of the usefulness of a prediction tool in levitation consciousness about Osteoporosis. CONCLUSIONS The presented work supplements the evidence base that an accessible prediction tool can help in raising disease awareness. In conjunction with knowledge provided by a medical doctor, OSPreKnow has beneficial impacts on improving the habits of women with risks of OS. CLINICALTRIAL Princess Nourah bint Abdulrahman University (Institutional Review Board [IRB] number 21-0265)

scholarly journals Analyzing User Digital Emotions from a Holy versus Non-Pilgrimage City in Saudi Arabia on Twitter Platform

2021 ◽  
Vol 11 (15) ◽  
pp. 6846
Author(s):  
Kashish Ara Shakil ◽  
Kahkashan Tabassum ◽  
Fawziah S. Alqahtani ◽  
Mudasir Ahmad Wani
Keyword(s):  
Saudi Arabia ◽  
Behavioral Differences ◽  
Initial Attempt ◽  
Bilingual Dictionary ◽  
Metropolitan Cities ◽  
Emotion Lexicon ◽  
User Data ◽  
Regional Location ◽  
Media Platform ◽  
Holy City

Humans are the product of what society and their environment conditions them into being. People living in metropolitan cities have a very fast-paced life and are constantly exposed to different situations. A social media platform enables individuals to express their emotions and sentiments and thus acts as a reservoir for the digital emotion footprints of its users. This study proposes that the user data available on Twitter has the potential to showcase the contrasting emotions of people residing in a pilgrimage city versus those residing in other, non-pilgrimage areas. We collected the Arabic geolocated tweets of users living in Mecca (holy city) and Riyadh (non-pilgrimage city). The user emotions were classified on the basis of Plutchik’s eight basic emotion categories, Fear, Anger, Sadness, Joy, Surprise, Disgust, Trust, and Anticipation. A new bilingual dictionary, AEELex (Arabic English Emotion Lexicon), was designed to determine emotions derived from user tweets. AEELex has been validated on commonly known and popular lexicons. An emotion analysis revealed that people living in Mecca had more positivity than those residing in Riyadh. Anticipation was the emotion that was dominant or most expressed in both places. However, a larger proportion of users living in Mecca fell under this category. The proposed analysis was an initial attempt toward studying the emotional and behavioral differences between users living in different cities of Saudi Arabia. This study has several other important applications. First, the emotion-based study could contribute to the development of a machine learning-based model for predicting depression in netizens. Second, behavioral appearances mined from the text could benefit efforts to identify the regional location of a particular user.

scholarly journals Institutional Review Board and International Field Research in Conflict Zones

2014 ◽  
Vol 47 (04) ◽  
pp. 840-844 ◽  
Author(s):  
Srobana Bhattacharya
Keyword(s):  
Human Subjects ◽  
Social Science Research ◽  
Field Research ◽  
Science Research ◽  
Political Conflict ◽  
Institutional Review Board ◽  
Biomedical Sciences ◽  
Conflict Zones ◽  
Institutional Review ◽  
The Many

ABSTRACTResearch on political conflict can benefit immensely from fieldwork. However, the Institutional Review Board (IRB) process is elaborate and daunting that discourages rather than encourages this type of research. Existing policies often are insensitive to the many uncertainties related to field research abroad, especially in conflict zones. Three reasons for this are identified in this article. First, the federal regulations to protect human subjects of social science research are most suitable for biomedical sciences. Second, there is huge gap between “procedural ethics” and “ethics in practice.” Third, there is a lack of communication or dialogue between researchers and IRBs. After discussing these reasons, I offer the following suggestions: bridging the gap between the researcher and the IRB; reducing delays in the IRB approval and revision process; encouraging collaboration and dialogue among researchers; and advocating a proactive stance by academic associations.

Reporting Practices in Quantitative Teacher Education Research: One Look at the Evidence Cited in the AERA Panel Report

2008 ◽  
Vol 37 (4) ◽  
pp. 208-216 ◽  
Author(s):  
Linda Reichwein Zientek ◽  
Mary Margaret Capraro ◽  
Robert M. Capraro
Keyword(s):  
Teacher Education ◽  
Social Science Research ◽  
Reliability And Validity ◽  
Science Research ◽  
Evidence Base ◽  
Effect Sizes ◽  
Research Articles ◽  
Reporting Practices ◽  
Journal Editors ◽  
Empirical Social Science

The authors of this article examine the analytic and reporting features of research articles cited in Studying Teacher Education: The Report of the AERA Panel on Research and Teacher Education ( Cochran-Smith & Zeichner, 2005b ) that used quantitative reporting practices. Their purpose was to help to identify reporting practices that can be improved to further the creation of the best possible evidence base for teacher education. Their findings indicate that many study reports lack (a) effect sizes, (b) confidence intervals, and (c) reliability and validity coefficients. One possible solution is for journal editors to emphasize clearly the expectations established in Standards for Reporting on Empirical Social Science Research in AERA Publications ( AERA, 2006 ).

Hypothetical Protocol: Behavioral and Social Science Research

1980 ◽  
Vol 59 (3_suppl) ◽  
pp. 1305-1306
Keyword(s):  
Social Science ◽  
Social Science Research ◽  
Science Research ◽  
Institutional Review Board ◽  
Institutional Review

An "add-on" study has been brought to the attention of the University's Institutional Review Board (IRB) which has approved Dr. A's study. As a member of the IRB, do you have any questions or concerns about the investigation?

scholarly journals Molecular tools for bathing water assessment in Europe: Balancing social science research with a rapidly developing environmental science evidence-base

AMBIO ◽  
2015 ◽  
Vol 45 (1) ◽  
pp. 52-62 ◽  
Author(s):  
David M. Oliver ◽  
Nick D. Hanley ◽  
Melanie van Niekerk ◽  
David Kay ◽  
A. Louise Heathwaite ◽  
...  
Keyword(s):  
Social Science ◽  
Environmental Science ◽  
Social Science Research ◽  
Science Research ◽  
Evidence Base ◽  
Molecular Tools ◽  
Bathing Water ◽  
Water Assessment ◽  
Science Evidence

A Collaborative Filtering Recommendation Algorithm Based on Cloud Model, Item Attribute and User Data

2014 ◽  
Vol 610 ◽  
pp. 747-751
Author(s):  
Jian Sun ◽  
Xiao Ying Chen
Keyword(s):  
Collaborative Filtering ◽  
Prediction Accuracy ◽  
Cloud Model ◽  
Prediction Algorithm ◽  
Recommendation Algorithm ◽  
Rating Data ◽  
User Data ◽  
Item Attribute ◽  
Rating Prediction ◽  
User Ratings

Aiming at the problems of extremely sparse of user-item rating data and poor recommendation quality, we put forward a collaborative filtering recommendation algorithm based on cloud model, item attribute and user data which combined with the existing literatures. A rating prediction algorithm based on cloud model and item attribute is proposed, based on idea that the similar users rating for the same item are similar and the same user ratings for the similar items are similar and stable. Through compare and analysis this paper’s and other studies experimental results, we get the conclusion that the rating prediction accuracy is improved.

scholarly journals Prediction of Acute Kidney Injury with a Machine Learning Algorithm using Electronic Health Record Data

2017 ◽  
Author(s):  
Hamid Mohamadlou ◽  
Anna Lynn-Palevsky ◽  
Christopher Barton ◽  
Uli Chettipally ◽  
Lisa Shieh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Sofa Score ◽  
Learning Algorithm ◽  
Medical Center ◽  
Kidney Injury ◽  
Kidney Damage ◽  
Prediction Algorithm ◽  
Prediction Tool ◽  
Electronic Health Record Data

AbstractBackgroundA major problem in treating acute kidney injury (AKI) is that clinical criteria for recognition are markers of established kidney damage or impaired function; treatment before such damage manifests is desirable. Clinicians could intervene during what may be a crucial stage for preventing permanent kidney injury if patients with incipient AKI and those at high risk of developing AKI could be identified.MethodsWe used a machine learning technique, boosted ensembles of decision trees, to train an AKI prediction tool on retrospective data from inpatients at Stanford Medical Center and intensive care unit patients at Beth Israel Deaconess Medical Center. We tested the algorithm’s ability to detect AKI at onset, and to predict AKI 12, 24, 48, and 72 hours before onset, and compared its 3-fold cross-validation performance to the SOFA score for AKI identification in terms of Area Under the Receiver Operating Characteristic (AUROC).ResultsThe prediction algorithm achieves AUROC of 0.872 (95% CI 0.867, 0.878) for AKI onset detection, superior to the SOFA score AUROC of 0.815 (P < 0.01). At 72 hours before onset, the algorithm achieves AUROC of 0.728 (95% CI 0.719, 0.737), compared to the SOFA score AUROC of 0.720 (P < 0.01).ConclusionsThe results of these experiments suggest that a machine-learning-based AKI prediction tool may offer important prognostic capabilities for determining which patients are likely to suffer AKI, potentially allowing clinicians to intervene before kidney damage manifests.

scholarly journals Regulating Bodies: Children and Sexual Violence

2019 ◽  
Vol 25 (16) ◽  
pp. 1956-1979
Author(s):  
Heather R. Hlavka
Keyword(s):  
Young People ◽  
Sexual Violence ◽  
Social Science Research ◽  
Science Research ◽  
Children And Youth ◽  
Institutional Review Boards ◽  
Child Sexual Assault ◽  
Institutional Review ◽  
Epistemic Violence ◽  
Children And Childhood

The interdisciplinary silences on sexual violence and the omission of children and youth from social science research speak volumes of the power of the child as a flexible, cultural signifier. In this article, I argue that dominant frameworks of children and childhood make child sexual assault a discursive impossibility for most young people. The epistemic violence of silencing matters, and it is these erasures that are fundamental to understanding violence and power. I argue it is paramount for feminist researchers to call attention to the undermining qualities of Institutional Review Boards that act as gatekeepers of representation and voice.

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