scholarly journals Researcher Perceptions of a Self-Service Online Portal to Facilitate Volunteer Recruitment into Clinical Trials

ACI Open ◽  
2021 ◽  
Vol 05 (02) ◽  
pp. e59-e66
Author(s):  
Srinivas Emani ◽  
Yichuan Grace Hsieh ◽  
Greg Estey ◽  
Holly M. Parker ◽  
Xiaofeng Zhang ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Regression Model ◽  
Behavioral Intention ◽  
Self Efficacy ◽  
Hierarchical Regression ◽  
Intention To Use ◽  
Hierarchical Regression Model ◽  
Volunteer Recruitment ◽  
Facilitating Conditions ◽  
Behavioral Intention To Use

Abstract Background Recruitment of volunteers is a major challenge for clinical trials. There has been increasing development and use of Internet-based portals in recruitment for clinical research. There has been little research on researcher use and perceptions of these portals. Objectives This study evaluated researcher perceptions of use of Rally, an Internet-based portal for clinical trial volunteer recruitment. Methods A cross-sectional survey was developed and implemented to understand researcher perceptions. From theoretical models of information technology use, the survey adopted items in four domains: ease of use, usefulness, facilitating conditions, and self-efficacy. The dependent variable was researchers' behavioral intention to use Rally. The survey captured characteristics of researchers such as gender, age, and role. It was implemented using the REDCap survey tool. An email invitation followed by three reminders was sent to researchers. A hierarchical regression model was applied to assess predictors of behavioral intention. Results The survey response rate was 35.6% (152 surveys received from 427 contacted researchers). In the hierarchical regression model, facilitating conditions and self-efficacy predicted behavioral intention (F (4,94) = 6.478; p <0.001). The model explained 21.6% of the variance in behavioral intention (R-square change = 21.3%, p <0.001). Conclusion Facilitating conditions and self-efficacy predicted researchers' behavioral intention to use Rally for volunteer recruitment into clinical trials. Future research should document best practices and strategies for enhancing researcher use of online portals for volunteer recruitment.

scholarly journals SCHiRM: Single Cell Hierarchical Regression Model to detect dependencies in read count data

2018 ◽  
Author(s):  
Jukka Intosalmi ◽  
Henrik Mannerström ◽  
Saara Hiltunen ◽  
Harri Lähdesmäki
Keyword(s):  
Regression Model ◽  
Single Cell ◽  
Count Data ◽  
Regulatory Networks ◽  
Research Question ◽  
Read Count ◽  
Hierarchical Regression ◽  
Specific Gene ◽  
Hierarchical Regression Model ◽  
Log Normal

AbstractMotivationModern single cell RNA sequencing (scRNA-seq) technologies have made it possible to measure the RNA content of individual cells. The scRNA-seq data provide us with detailed information about the cellular states but, despite several pioneering efforts, it remains an open research question how regulatory networks could be inferred from these noisy discrete read count data.ResultsHere, we introduce a hierarchical regression model which is designed for detecting dependencies in scRNA-seq and other count data. We model count data using the Poisson-log normal distribution and, by means of our hierarchical formulation, detect the dependencies between genes using linear regression model for the latent, cell-specific gene expression rate parameters. The hierarchical formulation allows us to model count data without artificial data transformations and makes it possible to incorporate normalization information directly into the latent layer of the model. We test the proposed approach using both simulated and experimental data. Our results show that the proposed approach performs better than standard regression techniques in parameter inference task as well as in variable selection task.AvailabilityAn implementation of the method is available athttps://github.com/jeintos/[email protected],[email protected]

scholarly journals The Influence of Clinical Nurses' Empathy on their Death Competence: A Cross-Sectional Survey Study

2021 ◽  
Author(s):  
Jiahao Wang ◽  
Zhengying Chen ◽  
Yiting Liu ◽  
Xiaoli Liao ◽  
Liuxin Long ◽  
...  
Keyword(s):  
Regression Model ◽  
Pearson Correlation ◽  
General Information ◽  
Survey Study ◽  
Hierarchical Regression ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Clinical Nurses ◽  
Hierarchical Regression Model ◽  
The Status

Abstract BackgroundEmpathy and death competence are important competences for clinical nurses. However, there is no clear consensus about what impact empathy has on death competency. Our study aimed to understand the status of the empathy and death competence of clinical nurses in China and to explore the effect of empathy on their competence.MethodsFor a survey conducted from May–June 2021, 1415 clinical nurses were selected by convenience sampling as the research objects. The Coping with Death Scale, the Jefferson Scale of Empathy—Health Professionals and a general information questionnaire designed by the researchers were used to investigate the status of the empathy and death competence of clinical nurses. The relationship between empathy and death competence was analysed by Pearson correlation, and the influence of the empathy of clinical nurses on their death competence was analysed by a hierarchical regression model.ResultPearson correlation analysis revealed that death competence was positively correlated with each dimension of empathy. Hierarchical regression model analysis revealed that after controlling for the influence of general information, nurses' empathy had a significant influence on their death competence, and this independently explained 5.8% of the variance in death competence.ConclusionsThe death competence of the clinical nurses in this sample was moderate to low level. Emotional nursing and transposition thinking are important influencing factors of death competence. Nursing managers should improve the empathy of clinical nurses to promote their death competence.

scholarly journals Malaria parasite clearance rate regression: an R software package for a Bayesian hierarchical regression model

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Saeed Sharifi-Malvajerdi ◽  
Feiyu Zhu ◽  
Colin B. Fogarty ◽  
Michael P. Fay ◽  
Rick M. Fairhurst ◽  
...  
Keyword(s):  
Regression Model ◽  
Clearance Rate ◽  
Software Package ◽  
Malaria Parasite ◽  
Parasite Clearance ◽  
Hierarchical Regression ◽  
R Software ◽  
Bayesian Hierarchical ◽  
Hierarchical Regression Model

Hispanic and Anglo Interviewer and Respondent Ethnicity and Gender: The Impact on Survey Response Quality

1996 ◽  
Vol 33 (1) ◽  
pp. 62-72 ◽  
Author(s):  
Cynthia Webster
Keyword(s):  
Regression Model ◽  
Survey Research ◽  
Hierarchical Regression ◽  
Sensitive Questions ◽  
Response Quality ◽  
Hierarchical Regression Model ◽  
And Gender ◽  
Ethnicity And Gender ◽  
Main Effects ◽  
The Impact

The author applies a hierarchical regression model to analyze the simultaneous effects of Hispanic and Anglo interviewer and respondent ethnicity and gender on response quality in survey research. Although few significant main effects were found, response quality was affected significantly by interaction effects of respondent and interviewer ethnicity and gender. For the most part, ethnically homophilous and gender-heterophilous interviews generate the highest response quality. Both Hispanic and Anglo respondents deferred to an interviewer of a different ethnic background when queried about the interviewer's culture, but not when asked noncultural, albeit sensitive, questions.

scholarly journals Reflexivity and flexibility: Complementary routes to innovation?

2015 ◽  
Vol 22 (3) ◽  
pp. 404-419 ◽  
Author(s):  
Maria Luisa Farnese ◽  
Roberta Fida ◽  
Stefano Livi
Keyword(s):  
Regression Model ◽  
Learning Process ◽  
Empirical Support ◽  
Innovation Adoption ◽  
Hierarchical Regression ◽  
Organisational Innovation ◽  
Complementary Resources ◽  
Hierarchical Regression Model ◽  
Innovation Outcomes

AbstractFlexibility and reflexivity are essential processes for organisational innovation. The aim of the paper is to investigate their concurrent and interactive contribution in enhancing two innovation outcomes (the organisational openness towards innovation and the actual innovation adoption). Participants were 357 Italian employees. Results of a hierarchical regression model showed the role of both factors in fostering the two innovation outcomes under study. In addition, results showed the complementary interaction of reflexivity and flexibility, outlining two possible routes to innovation. Specifically, reflexivity appears to be a generative learning process capable of encouraging innovation in low-flexibility conditions, whereas flexibility tends to encourage innovation in low-reflexivity conditions. The findings provide empirical support of their roles as complementary resources for innovation, which has been under-examined in the literature.

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