bluuebunny/crossref_metadata_2025_split huggingface dataset:
Addressing the interaction between food insecurity, perinatal depression and informal work: findings of a cross-sectional survey among informal women workers with young children in South Africa.
Authors: Horwood Christiane , Lyn Haskins , Rachael Hinton , Catherine Connolly , Silondile Luthuli , Nigel Rollins 
Published: April 2020

Abstract
Background: There is a high burden of depression globally, including in South Africa. Maternal depression is associated with poverty, unstable income, food insecurity, and lack of partner support, and may lead to poor outcomes for mothers and children. In South Africa one-third of working women are in informal work, which is associated with socioeconomic vulnerability.Methods: A cross sectional survey explored work setting and conditions, food security and risk of depression among informal working women with young children (0-3years). Depression risk was assessed using the Edinburgh Postnatal Depression Score (EPDS) and Whooley score. Food insecurity was evaluated using USAID Household Food Insecurity Access Scale (HIFAS). Data was analysed using SPSS and STATA.Results: Interviews were conducted with 265 informal women workers. Types of work included domestic work, home-based work, informal employees and own account workers, most of whom were informal traders. Most participants (149/265; 56.2%) earned between US$70-200 per month, but some participants (79/265; 29.8%) earned less than US$70 per month, and few earned >US$200 per month (37/265; 14.0%). Many participants experienced mild (38/267; 14.3%), moderate (72/265; 27.2%) or severe (43/265; 16%) food insecurity. Severe food insecurity was significantly higher among participants with the lowest income compared to those with the highest income (p= 0.027). Women who received financial support from the baby’s father were less likely to be food insecure (p=0.03). Using EPDS scores, 22/265 (8.3%) women were designated as being at risk of depression. This was similar among postnatal women and women with older children. Household food insecurity was significantly associated with depression risk (p<0.001).Conclusions: Informal women workers were shown to be vulnerable with low incomes and high rates of food insecurity, thus increasing the risk for poor maternal health, and child health and development outcomes. However, levels of depression risk were low compared to previous estimates in South Africa, suggesting that informal workers may have high levels of resilience. Interventions to improve social protection, access to health services, and support for safe childcare in the workplace could support these mothers care for their children.
Determination of the effect size of an observed factor based on a multi-variate model for evaluating practical significance of differences between two groups of case-control design
Authors: Hui Liu 
Published: April 2020

Abstract
Background: To determine the effect size of an observed factor for a disease by using consistency in a cohort study (CRC) for evaluating practical significance of differences between two groups of case-control design. Methods: A model of multiple pathogenic factors was established by analyzing the number and distribution of observed factors in a study population. The difference in the incidence between two groups (exposed and unexposed) was calculated according to the model as CRC. The relationship of Youden’s index and true and false-positive ratio (TFR) in case-control design were observed with CRC. Results: The CRC was able to correctly reflect the number of factors combined in the models, and therefore, indicates that CRC is a reasonable indicator of effect size. Difference scores <0.25 indicate that one of four or more factors plays a role in a disease; scores >0.50 indicate one of two factors plays a role in disease and implies a high intensity level of the factor. TFR could correctly reflect CRC. Accordingly, a factor with an effect size (i.e., TFR) less than 6.0 should not be considered a clinically significant factor, even if the observed difference is statistically significant. Conclusions: A CRC over 0.25 OR TFR over 6.0 is suggested as an indicator of a substantial effect size.
Application of Principal Component Analysis and Logistic Regression Model in Lupus Nephritis Patients with Clinical Hypothyroidism
Authors: Huang Ting , Li Jiarong , Zhang Weiru 
Published: April 2020

Abstract
Background : Previous studies indicate that the prevalence of hypothyroidism is much higher in patients with lupus nephritis (LN) than in the general population, and is associated with LN’s activity. Principal component analysis (PCA) and logistic regression can help determine relevant risk factors and identify LN patients at high risk of hypothyroidism; as such, these tools may prove useful in managing this disease. Methods: We carried out a cross-sectional study of 143 LN patients diagnosed by renal biopsy, all of whom had been admitted to Xiangya Hospital of Central South University in Changsha, China, between June 2012 and December 2016. The PCA–logistic regression model was used to determine the influential principal components for LN patients who have hypothyroidism. Results : Our PCA–logistic regression analysis results demonstrated that serum creatinine, blood urea nitrogen, blood uric acid, total protein, albumin, and anti-ribonucleoprotein antibody were important clinical variables for LN patients with hypothyroidism. The area under the curve of this model was 0.855. Conclusion : The PCA–logistic regression model performed well in identifying important risk factors for certain clinical outcomes, and promoting clinical research on other diseases will be beneficial. Using this model, clinicians can identify at-risk subjects and either implement preventative strategies or manage current treatments.
Dynamic RMST Curves for Survival Analysis in Clinical Trials
Authors: Liao Jason , Liu G. Frank , Wu Wen-Chi 
Published: April 2020

Abstract
Background The data from immuno-oncology therapy trials often show delayed effects, cure rate, crossing hazards, or some mixture of these phenomena. Thus, the common assumption of proportional hazards is often violated such that the commonly used log-rank test can be very underpowered. In these trials, the conventional hazard ratio for describing the treatment effect may not be a good estimand due to the lack of an easily understandable interpretation. To overcome this challenge, restricted mean survival time (RMST) has been strongly recommended for survival analysis in clinical literature due to its independence of the proportional hazard assumption as well as a more clinically meaningful interpretation. The RMST also aligns well with the estimand associated with the analysis from the recommendation in ICH E-9 (R1), and the test/estimation coherency. Currently, the Kaplan Meier (KM) curve is commonly applied to RMST related analyses. Due to some drawbacks of the KM approach such as the limitation in extrapolating to time points beyond the follow-up time, and the large variance at time points with small numbers of events, the RMST may be hindered.Methods To fully enhance the RMST method, the survival curve using a mixture model is proposed in this paper to construct dynamic RMST curves to evaluate and monitor survival analysis in clinical trials.Results This new dynamic RMST curve overcomes the drawbacks from the KM approach. The good performance of this new proposal is illustrated through three real examples.Conclusions RMST provides a clinically meaningful and easily interpretable measure for survival clinical trials. The proposed dynamic RMST approach provides a useful tool for assessing treatment effect over different time frames for survival clinical trials. This dynamic RMST curve can also be useful for checking whether the follow-up time for a study is long enough to demonstrate a treatment difference. The prediction feature of the dynamic RMST analysis may also be used for determining an appropriate time point for an interim analysis , and an evaluation tool for study recommendation from DMC .
Use of Missing Indicators as Proxies for Unmeasured Variables: Simulation Study
Authors: Sperrin Matthew , Martin Glen P. 
Published: April 2020

Abstract
Background Within routinely collected health data, missing data for an individual might provide useful information in itself. This occurs, for example, in the case of electronic health records, where the presence or absence of data is informative. While the naive use of missing indicators to try to exploit such information can introduce bias when used inappropriately, its use in conjunction with other imputation approaches may unlock the potential value of missingness to reduce bias and improve prediction.Methods We conducted a simulation study to determine when the use of a missing indicator, combined with an imputation approach, such as multiple imputation, would lead to improved model performance, in terms of minimising bias for causal effect estimation, and improving predictive accuracy, under a range of scenarios with unmeasured variables. We use directed acyclic graphs and structural models to elucidate causal structures of interest. We consider a variety of missingness mechanisms, then handle these using complete case analysis, unconditional mean imputation, regression imputation and multiple imputation. In each case we evaluate supplementing these approaches with missing indicator terms. Results For estimating causal effects, we find that multiple imputation combined with a missing indicator gives minimal bias in most scenarios. For prediction, we find that regression imputation combined with a missing indicator minimises mean squared error.Conclusion In the presence of missing data, careful use of missing indicators, combined with appropriate imputation, can improve both causal estimation and prediction accuracy.
Barriers and facilitators in the usability of an integrated blended care program in patients with medically unexplained physical symptoms from the healthcare professionals’ perspective; a qualitative study
Authors: Toonders Suze Adriana Johanna , Poolman Eva Y , Nieboer Marianne E , Pisters Martijn Frits , Veenhof Cindy 
Published: April 2020

Abstract
Background: Increasingly, healthcare policies have changed focus from cure and care to behaviour and health. Prevention is becoming more important, which requires a change in the role of healthcare professionals. Healthcare professionals’ role is changing from being a therapist to taking on the role of a coach. To prevent chronicity in Medically Unexplained Physical Symptoms (MUPS), an integrated blended care program was developed. To apply this new program in daily practice, it is important to gain insight into the usability. From the healthcare professionals’ point of view the concept of usability consists of performance, satisfaction and acceptability. Methods: Data were collected from semi-structured interviews, which were recorded and transcribed. Data were analysed manually and independently by two researchers. Results: Ten healthcare professionals (six physical therapists and four mental health nurses) were interviewed. Six core themes on usability were identified: (1) selection and motivation of patients, (2) training and doing, (3) program procedure, (4) maturity of the e-Coaching application, 5) interprofessional collaboration and (6) expectations and experiences. Conclusion: An integrated blended care program offers the possibility to personalize treatment. This study gathered healthcare professionals’ experiences with and attitudes towards integrating healthcare and offering blended care programs. Findings show attention should be given to the new responsibilities of healthcare professionals, and their role in integrated and blended care. This new approach of delivering healthcare can facilitate interprofessional collaboration. Achieving sustainable change in patients however still requires instruction and support for healthcare professionals implementing behavioural change techniques. Trail registration: The study was approved by the Medical Ethical Committee Utrecht, by number 17/391. Registered 14 June 2017, https://www.metc-utrecht.nl/
Knowledge regarding 2019 novel coronavirus (2019-nCoV) infection among final year health science students at Arbaminch College of Health Sciences, Southern Ethiopia: a cross-sectional study
Authors: Wogayehu Biruk , Taye Wondimagegn , chisha Yilma , Faraja Kahssay 
Published: April 2020

Abstract
Introduction In January 2020, World Health Organization declared the outbreak of novel coronavirus a pandemic (global health emergency). The aim of this study was to assess the knowledge about novel coronavirus and its determinant factors among health science students at Arbaminch Health Sciences College, Southwest Ethiopia.Method A cross-sectional study design was employed to assess the level knowledge about novel coronavirus among 304 graduating class students in Arbaminch Health Science College. The study participants were selected using a simple random sampling technique. The data collection tool consisted of 33 items (10 items about demographic and education related and 23 items about knowledge on novel coronavirus). Multivariable logistic regression was performed using SPSS.Results The vast majority of students 228(75%) scored below 50% and were considered to have poor knowledge on COVID-19. Only 7.6% of participants knew that muscle pain is a symptom of 2019-nCoV infection. One hundred forty three (47%) of the participants did not know any symptoms of COVID-19. Only 24.3% of the respondents answered correctly that rubbing hands with alcohol based sanitizers can help in prevention of disease transmission. Sex, residence and social media use were significantly associated with adequate knowledge on novel coronavirus (p < 0.05).Conclusion Health science students had poor knowledge on COVID-19. The health science college and health authorities should re-examine their capability to manage the deadly virus
Development and Validation of a Comprehensive Well-Being Scale for People in the University Environment (Pitt Wellness Scale) Using a Crowdsourcing Approach: Cross-Sectional Study
Authors: Zhou Leming , Parmanto Bambang 
Published: April 2020

Background
Well-being has multiple domains, and these domains are unique to the population being examined. Therefore, to precisely assess the well-being of a population, a scale specifically designed for that population is needed.

Objective
The goal of this study was to design and validate a comprehensive well-being scale for people in a university environment, including students, faculty, and staff.

Methods
A crowdsourcing approach was used to determine relevant domains for the comprehensive well-being scale in this population and identify specific questions to include in each domain. A web-based questionnaire (Q1) was used to collect opinions from a group of university students, faculty, and staff about the domains and subdomains of the scale. A draft of a new well-being scale (Q2) was created in response to the information collected via Q1, and a second group of study participants was invited to evaluate the relevance and clarity of each statement. A newly created well-being scale (Q3) was then used by a third group of university students, faculty, and staff. A psychometric analysis was performed on the data collected via Q3 to determine the validity and reliability of the well-being scale.

Results
In the first step, a group of 518 university community members (students, faculty, and staff) indicated the domains and subdomains that they desired to have in a comprehensive well-being scale. In the second step, a second group of 167 students, faculty, and staff evaluated the relevance and clarity of the proposed statements in each domain. In the third step, a third group of 546 students, faculty, and staff provided their responses to the new well-being scale (Pitt Wellness Scale). The psychometric analysis indicated that the reliability of the well-being scale was high.

Conclusions
Using a crowdsourcing approach, we successfully created a comprehensive and highly reliable well-being scale for people in the university environment. Our new Pitt Wellness Scale may be used to measure the well-being of people in the university environment.
Document-Level Biomedical Relation Extraction Using Graph Convolutional Network and Multihead Attention: Algorithm Development and Validation
Authors: Wang Jian , Chen Xiaoyu , Zhang Yu , Zhang Yijia , Wen Jiabin , Lin Hongfei , Yang Zhihao , Wang Xin 
Published: April 2020

Background
Automatically extracting relations between chemicals and diseases plays an important role in biomedical text mining. Chemical-disease relation (CDR) extraction aims at extracting complex semantic relationships between entities in documents, which contain intrasentence and intersentence relations. Most previous methods did not consider dependency syntactic information across the sentences, which are very valuable for the relations extraction task, in particular, for extracting the intersentence relations accurately.

Objective
In this paper, we propose a novel end-to-end neural network based on the graph convolutional network (GCN) and multihead attention, which makes use of the dependency syntactic information across the sentences to improve CDR extraction task.

Methods
To improve the performance of intersentence relation extraction, we constructed a document-level dependency graph to capture the dependency syntactic information across sentences. GCN is applied to capture the feature representation of the document-level dependency graph. The multihead attention mechanism is employed to learn the relatively important context features from different semantic subspaces. To enhance the input representation, the deep context representation is used in our model instead of traditional word embedding.

Results
We evaluate our method on CDR corpus. The experimental results show that our method achieves an F-measure of 63.5%, which is superior to other state-of-the-art methods. In the intrasentence level, our method achieves a precision, recall, and F-measure of 59.1%, 81.5%, and 68.5%, respectively. In the intersentence level, our method achieves a precision, recall, and F-measure of 47.8%, 52.2%, and 49.9%, respectively.

Conclusions
The GCN model can effectively exploit the across sentence dependency information to improve the performance of intersentence CDR extraction. Both the deep context representation and multihead attention are helpful in the CDR extraction task.
COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data
Authors: Ahmed Wasim , Vidal-Alaball Josep , Downing Joseph , López Seguí Francesc 
Published: April 2020

Background
Since the beginning of December 2019, the coronavirus disease (COVID-19) has spread rapidly around the world, which has led to increased discussions across online platforms. These conversations have also included various conspiracies shared by social media users. Amongst them, a popular theory has linked 5G to the spread of COVID-19, leading to misinformation and the burning of 5G towers in the United Kingdom. The understanding of the drivers of fake news and quick policies oriented to isolate and rebate misinformation are keys to combating it.

Objective
The aim of this study is to develop an understanding of the drivers of the 5G COVID-19 conspiracy theory and strategies to deal with such misinformation.

Methods
This paper performs a social network analysis and content analysis of Twitter data from a 7-day period (Friday, March 27, 2020, to Saturday, April 4, 2020) in which the #5GCoronavirus hashtag was trending on Twitter in the United Kingdom. Influential users were analyzed through social network graph clusters. The size of the nodes were ranked by their betweenness centrality score, and the graph’s vertices were grouped by cluster using the Clauset-Newman-Moore algorithm. The topics and web sources used were also examined.

Results
Social network analysis identified that the two largest network structures consisted of an isolates group and a broadcast group. The analysis also revealed that there was a lack of an authority figure who was actively combating such misinformation. Content analysis revealed that, of 233 sample tweets, 34.8% (n=81) contained views that 5G and COVID-19 were linked, 32.2% (n=75) denounced the conspiracy theory, and 33.0% (n=77) were general tweets not expressing any personal views or opinions. Thus, 65.2% (n=152) of tweets derived from nonconspiracy theory supporters, which suggests that, although the topic attracted high volume, only a handful of users genuinely believed the conspiracy.

This paper also shows that fake news websites were the most popular web source shared by users; although, YouTube videos were also shared. The study also identified an account whose sole aim was to spread the conspiracy theory on Twitter.

Conclusions
The combination of quick and targeted interventions oriented to delegitimize the sources of fake information is key to reducing their impact. Those users voicing their views against the conspiracy theory, link baiting, or sharing humorous tweets inadvertently raised the profile of the topic, suggesting that policymakers should insist in the efforts of isolating opinions that are based on fake news. Many social media platforms provide users with the ability to report inappropriate content, which should be used. This study is the first to analyze the 5G conspiracy theory in the context of COVID-19 on Twitter offering practical guidance to health authorities in how, in the context of a pandemic, rumors may be combated in the future.
The Effect of Attitude, Normal Subjective and Perceived Behavioral Control (Pbc) on Actual Purchasing Through Purchases of Online Purchase in The Online Retail Industry
Authors: HEPTARIZA ANITA 
Published: April 2020

Online shopping activities are currently growing. Online shopping activities are supported by the increasing number of Internet users. With the growing number of people who know the internet and along with the presence of Generation Z who was born in the digital age make the habit of spending goods and services slowly but surely have to switch online. One of the factors that affect online shopping is the intention of purchasing online. Theory of Planned Behavior (TPB) explains that consumer behavior is shaped by attitudes, subjective norms, and perceived behavioral control (PBC) that form the intention of online purchasing. This study aims to analyze the effect of attitudes on online purchase intentions, to analyze the effect of subjective norms on online purchasing intentions, to analyze the effect of PBC on online purchase intentions and to analyze the effect of online purchase intentions on actual purchases. The research was conducted at Prama Sanur Beach Hotel. The technique of data collection used are observation, interview, documentation, questionnaires. Data analysis technique using quantitative analysis with PLS program. The results show that attitudes have a positive and significant influence on the intentions of online purchasing. Subjective norms have a positive but insignificant effect on online purchase intentions. Perceived Behavioral Control (PBC) has a positive and significant effect on online purchasing intentions, and online purchase intentions have a positive and significant effect on actual purchases.

Keywords: Attitude, Subjective Norm, Perceived Behavioral Control (PBC), Intention Online Purchase, Actual Purchase.