Crowdsourcing for Research: Perspectives From a Delphi Panel

Crowdsourcing, an open call for the public to collaborate and participate in problem solving, has been increasingly employed as a method in health-related research studies. Various reviews of the literature across different disciplines found crowdsourcing being used for data collection, processing, and analysis as well as tasks such as problem solving, data processing, surveillance/monitoring, and surveying. Studies on crowdsourcing tend to focus on its use of software, technology and online platforms, or its application for the purposes previously noted. There is need for further exploration to understand how best to use crowdsourcing for research, as there is limited guidance for researchers who are undertaking crowdsourcing for the purposes of scientific study. Numerous authors have identified gaps in research related to crowdsourcing, including a lack of decision aids to assist researchers using crowdsourcing, and best-practice guidelines. This exploratory study looks at crowdsourcing as a research method by understanding how and why it is being used, through application of a modified Delphi technique. It begins to articulate how crowdsourcing is applied in practice by researchers, and its alignment with existing research methods. The result is a conceptual framework for crowdsourcing, developed within traditional and existing research approaches as a first step toward its use in research

An Exploration of the Application of Crowdsourcing to Health-Related Research

Background: A growing number of health research projects are employing crowdsourcing as part of their methods, leveraging it to inform everything from study design to participant recruitment to data collection and analysis. Therefore, greater understanding of how crowdsourcing is being used and how it can be applied in the research contexts warrants further exploration. Purpose: The purpose of this dissertation was to explore crowdsourcing as a means of research inquiry, and to locate it amidst research paradigms; understand how crowdsourcing in research is used in practice; and, create a framework, and guidelines, for researchers using crowdsourcing in their research. Research Questions: The following research questions were posed: a) What are the core principles and philosophies of crowdsourcing as a research paradigm? b) How and why are researchers using crowdsourcing? c) How are researchers addressing the basic characteristic of crowdsourcing in research studies? d) How could researcher address the basic characteristics of crowdsourcing in research studies? Methodology: To answer the first question, the ontology, epistemology, methodology and axiology of crowdsourcing as a research paradigm was explored. An observational study then analyzed 227 publically available research projects on a crowdsourcing website. Finally, a modified Delphi technique was used to determine whether there was a consensus among 18 experts regarding the use of crowdsourcing for the purposes of research. Based on these studies, a conceptual framework for crowdsourcing research studies emerged. Findings: The core principles and philosophies of crowdsourcing resemble those of the participatory paradigm. Crowdsourcing is being used primarily as a method for participant recruitment, data collection and analysis. The most plausible framework for the application of crowdsourcing in studies is based on the research paradigm which in turn defines the roles of the crowd. The role of the crowd defined in generally acceptable research terms (i.e. participant, data collection, analysis, study design etc.) makes it feasible to align the role with the research paradigms to define the crowd as subjects or participants, citizen scientists, or co-researchers. Implications: These findings suggest that crowdsourcing as a method should align with the research paradigm within which it is being applied. Implications for future research are discussed

Comparing Attributional and Relational Similarity as a Means to Identify Clinically Relevant Drug-gene Relationships

In emerging domains, such as precision oncology, knowledge extracted from explicit assertions may be insufficient to identify relationships of interest. One solution to this problem involves drawing inference on the basis of similarity. Computational methods have been developed to estimate the semantic similarity and relatedness between terms and relationships that are distributed across corpora of literature such as Medline abstracts and other forms of human readable text. Most research on distributional similarity has focused on the notion of attributional similarity, which estimates the similarity between entities based on the contexts in which they occur across a large corpus. A relatively under-researched area concerns relational similarity, in which the similarity between pairs of entities is estimated from the contexts in which these entity pairs occur together. While it seems intuitive that models capturing the structure of the relationships between entities might mediate the identification of biologically important relationships, there is to date no comparison of the relative utility of attributional and relational models for this purpose. In this research, I compare the performance of a range of relational and attributional similarity methods, on the task of identifying drugs that may be therapeutically useful in the context of particular aberrant genes, as identified by a team of human experts. My hypothesis is that relational similarity will be of greater utility than attributional similarity as a means to identify biological relationships that may provide answers to clinical questions, (such as “which drugs INHIBIT gene x”?) in the context of rapidly evolving domains. My results show that models based on relational similarity outperformed models based on attributional similarity on this task. As the methods explained in this research can be applied to identify any sort of relationship for which cue pairs exist, my results suggest that relational similarity may be a suitable approach to apply to other biomedical problems. Furthermore, I found models based on neural word embeddings (NWE) to be particularly useful for this task, given their higher performance than Random Indexing-based models, and significantly less computational effort needed to create them. NWE methods (such as those produced by the popular word2vec tool) are a relatively recent development in the domain of distributional semantics, and are considered by many as the state-of-the-art when it comes to semantic language modeling. However, their application in identifying biologically important relationships from Medline in general, and specifically, in the domain of precision oncology has not been well studied. The results of this research can guide the design and implementation of biomedical question answering and other relationship extraction applications for precision medicine, precision oncology and other similar domains, where there is rapid emergence of novel knowledge. The methods developed and evaluated in this project can help NLP applications provide more accurate results by leveraging corpus based methods that are by design scalable and robust

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

Timely and reliable evaluation of the effects of interventions: a framework for adaptive meta-analysis (FAME)

Most systematic reviews are retrospective and use aggregate data AD) from publications, meaning they can be unreliable, lag behind therapeutic developments and fail to influence ongoing or new trials. Commonly, the potential influence of unpublished or ongoing trials is overlooked when interpreting results, or determining the value of updating the meta-analysis or need to collect individual participant data (IPD). Therefore, we developed a Framework for Adaptive Metaanalysis (FAME) to determine prospectively the earliest opportunity for reliable AD meta-analysis. We illustrate FAME using two systematic reviews in men with metastatic (M1) and non-metastatic (M0)hormone-sensitive prostate cancer (HSPC)

2020 Student Symposium Research and Creative Activity Book of Abstracts

The UMaine Student Symposium (UMSS) is an annual event that celebrates undergraduate and graduate student research and creative work. Students from a variety of disciplines present their achievements with video presentations. It’s the ideal occasion for the community to see how UMaine students’ work impacts locally – and beyond. The 2020 Student Symposium Research and Creative Activity Book of Abstracts includes a complete list of student presenters as well as abstracts related to their works

Information Technology's Role in Global Healthcare Systems

Over the past few decades, modern information technology has made a significant impact on people’s daily lives worldwide. In the field of health care and prevention, there has been a progressing penetration of assistive health services such as personal health records, supporting apps for chronic diseases, or preventive cardiological monitoring. In 2020, the range of personal health services appeared to be almost unmanageable, accompanied by a multitude of different data formats and technical interfaces. The exchange of health-related data between different healthcare providers or platforms may therefore be difficult or even impossible. In addition, health professionals are increasingly confronted with medical data that were not acquired by themselves, but by an algorithmic “black box”. Even further, externally recorded data tend to be incompatible with the data models of classical healthcare information systems.From the individual’s perspective, digital services allow for the monitoring of their own health status. However, such services can also overwhelm their users, especially elderly people, with too many features or barely comprehensible information. It therefore seems highly relevant to examine whether such “always at hand” services exceed the digital literacy levels of average citizens.In this context, this reprint presents innovative, health-related applications or services emphasizing the role of user-centered information technology, with a special focus on one of the aforementioned aspects