Using social values in the prioritization of research: Quantitative examples and generalizations.
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ABSTRACT:
SUBMITTER: Falcy MR
PROVIDER: S-EPMC8717289 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundDespite the existence of safe and effective vaccines, rabies disease still causes an estimated 59,000 human deaths a year in the endemic areas in Asia and Africa. These numbers reflect severe drawbacks regarding the implementation of PrEP and PEP in endemic settings, such as lack of political will and low priority given to rabies. Since these contextual factors have proven to be persistent, there is an urgency to improve current strategies or develop novel approaches in order to control rabies disease in the future.Methods/findingsThis study aimed to identify and systematically prioritize the research needs, through interviews and questionnaires with key-opinion-leaders (KOLs). A total of 46 research needs were identified and prioritized. The top research needs are considered very high priority based on both importance for rabies control and need for improvement. KOLs agree that animal rabies control remains most important for rabies control, while research on human host, agent (rabies virus) and the environment should be prioritized in terms of need for improvement. A wide variety in perceptions is observed between and within the disciplines of virology, public health and veterinary health and between KOLs with more versus those with less experience in the field.Conclusion/significanceThe results of this study give well-defined, prioritized issues that stress the drawbacks that are experienced by KOLs in daily practice. The most important research domains are: 1) cheap and scalable production system for RIG 2) efficacy of dog mass vaccination programs and 3) cheap human vaccines. Addressing these research needs should exist next to and may reinforce current awareness and mass vaccination campaigns. The differences in perspectives between actors revealed in this study are informative for effective execution of the One Health research agenda.
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Project description:Evidence from empirical literature suggests that explainable complex behaviors can be built from structured compositions of explainable component behaviors with known properties. Such component behaviors can be built to directly perceive and exploit affordances. Using six examples of recent research in legged robot locomotion, we suggest that robots can be programmed to effectively exploit affordances without developing explicit internal models of them. We use a generative framework to discuss the examples, because it helps us to separate-and thus clarify the relationship between-description of affordance exploitation from description of the internal representations used by the robot in that exploitation. Under this framework, details of the architecture and environment are related to the emergent behavior of the system via a generative explanation. For example, the specific method of information processing a robot uses might be related to the affordance the robot is designed to exploit via a formal analysis of its control policy. By considering the mutuality of the agent-environment system during robot behavior design, roboticists can thus develop robust architectures which implicitly exploit affordances. The manner of this exploitation is made explicit by a well constructed generative explanation.
| S-EPMC7044146 | biostudies-literature
Project description:Computational approaches have recently gained popularity in the field of read-across to automatically fill data-gaps for untested chemicals. Previously, we developed the generalized read-across (GenRA) tool, which utilizes in vitro bioactivity data in conjunction with chemical descriptor information to derive local validity domains to predict hazards observed in in vivo toxicity studies. Here, we modified GenRA to quantitatively predict point of departure (POD) values obtained from US EPA's Toxicity Reference Database (ToxRefDB) version 2.0. To evaluate GenRA predictions, we first aggregated oral Lowest Observed Adverse Effect Levels (LOAEL) for 1,014 chemicals by systemic, developmental, reproductive, and cholinesterase effects. The mean LOAEL values for each chemical were converted to log molar equivalents. Applying GenRA to all chemicals with a minimum Jaccard similarity threshold of 0.05 for Morgan fingerprints and a maximum of 10 nearest neighbors predicted systemic, developmental, reproductive, and cholinesterase inhibition min aggregated LOAEL values with R2 values of 0.23, 0.22, 0.14, and 0.43, respectively. However, when evaluating GenRA locally to clusters of structurally-similar chemicals (containing 2 to 362 chemicals), average R2 values for systemic, developmental, reproductive, and cholinesterase LOAEL predictions improved to 0.73, 0.66, 0.60 and 0.79, respectively. Our findings highlight the complexity of the chemical-toxicity landscape and the importance of identifying local domains where GenRA can be used most effectively for predicting PODs.
| S-EPMC8565596 | biostudies-literature
Project description:BACKGROUND: Elucidating the genetic basis of human diseases is a central goal of genetics and molecular biology. While traditional linkage analysis and modern high-throughput techniques often provide long lists of tens or hundreds of disease gene candidates, the identification of disease genes among the candidates remains time-consuming and expensive. Efficient computational methods are therefore needed to prioritize genes within the list of candidates, by exploiting the wealth of information available about the genes in various databases. RESULTS: We propose ProDiGe, a novel algorithm for Prioritization of Disease Genes. ProDiGe implements a novel machine learning strategy based on learning from positive and unlabeled examples, which allows to integrate various sources of information about the genes, to share information about known disease genes across diseases, and to perform genome-wide searches for new disease genes. Experiments on real data show that ProDiGe outperforms state-of-the-art methods for the prioritization of genes in human diseases. CONCLUSIONS: ProDiGe implements a new machine learning paradigm for gene prioritization, which could help the identification of new disease genes. It is freely available at http://cbio.ensmp.fr/prodige.
| S-EPMC3215680 | biostudies-literature
Project description:Individuals, communities, and societies ascribe a diverse array of values to landscapes. These values are shaped by the aesthetic, cultural, and recreational benefits and services provided by those landscapes. However, across the globe, processes such as urbanization, agricultural intensification, and abandonment are threatening landscape integrity, altering the personally meaningful connections people have toward specific places. Existing methods used to study landscape values, such as social surveys, are poorly suited to capture dynamic landscape-scale processes across large geographic extents. Social media data, by comparison, can be used to indirectly measure and identify valuable features of landscapes at a regional, continental, and perhaps even worldwide scale. We evaluate the usefulness of different social media platforms-Panoramio, Flickr, and Instagram-and quantify landscape values at a continental scale. We find Panoramio, Flickr, and Instagram data can be used to quantify landscape values, with features of Instagram being especially suitable due to its relatively large population of users and its functional ability of allowing users to attach personally meaningful comments and hashtags to their uploaded images. Although Panoramio, Flickr, and Instagram have different user profiles, our analysis revealed similar patterns of landscape values across Europe across the three platforms. We also found variables describing accessibility, population density, income, mountainous terrain, or proximity to water explained a significant portion of observed variation across data from the different platforms. Social media data can be used to extend our understanding of how and where individuals ascribe value to landscapes across diverse social, political, and ecological boundaries.
| S-EPMC5135303 | biostudies-literature
Project description:BackgroundMuch has been written about public involvement (PI) in health and social care research, but underpinning values are rarely made explicit despite the potential for these to have significant influence on the practice and assessment of PI.ObjectiveThe narrative review reported here is part of a larger MRC-funded study which is producing a framework and related guidance on assessing the impact of PI in health and social care research. The review aimed to identify and characterize the range of values associated with PI that are central elements of the framework.MethodsWe undertook a review and narrative synthesis of diverse literatures of PI in health and social care research, including twenty existing reviews and twenty-four chapters in sixteen textbooks.ResultsThree overarching value systems were identified, each containing five value clusters. (i) A system concerned with ethical and/or political issues including value clusters associated with empowerment; change/action; accountability/transparency; rights; and ethics (normative values). (ii). A system concerned with the consequences of public involvement in research including value clusters associated with effectiveness; quality/relevance; validity/reliability; representativeness/objectivity/generalizability; and evidence (substantive values). (iii) A system concerned with the conduct of public involvement in including value clusters associated with Partnership/equality; respect/trust; openness and honesty; independence; and clarity (process values).ConclusionOur review identified three systems associated with PI in health and social care research focused on normative, substantive and process values. The findings suggest that research teams should consider and make explicit the values they attach to PI in research and discuss ways in which potential tensions may be managed in order to maximize the benefits of PI for researchers, lay experts and the research.
| S-EPMC5060838 | biostudies-literature
Project description:BackgroundAlthough cardiac resynchronization therapy (CRT) is effective for some patients with heart failure and a reduced left ventricular ejection fraction (HFrEF), evidence gaps remain for key clinical and policy areas. The objective of the study was to review the data on the effects of CRT for patients with HFrEF receiving pharmacological therapy alone or pharmacological therapy and an implantable cardioverter-defibrillator (ICD) and then, informed by a diverse group of stakeholders, to identify evidence gaps, prioritize them, and develop a research plan.MethodsRelevant studies were identified using PubMed and EMBASE and ongoing trials using clinicaltrials.gov. Forced-ranking prioritization method was applied by stakeholders to reach a consensus on the most important questions. Twenty-six stakeholders contributed to the expanded list of evidence gaps, including key investigators from existing randomized controlled trials and others representing different perspectives, including patients, the public, device manufacturers, and policymakers.ResultsOf the 18 top-tier evidence gaps, 8 were related to specific populations or subgroups of interest. Seven were related to the comparative effectiveness and safety of CRT interventions or comparators, and 3 were related to the association of CRT treatment with specific outcomes. The association of comorbidities with CRT effectiveness ranked highest, followed by questions about the effectiveness of CRT among patients with atrial fibrillation and the relationship between gender, QRS morphology and duration, and outcomes for patients either with CRT plus ICD or with ICD.ConclusionsEvidence gaps presented in this article highlight numerous, important clinical and policy questions for which there is inconclusive evidence on the role of CRT and provide a framework for future collaborative research.
| S-EPMC8667052 | biostudies-literature
Project description:Most people in the United States recognize the reality of climate change and are concerned about its consequences, yet climate change is a low priority relative to other policy issues. Recognizing that belief in climate change does not necessarily translate to prioritizing climate policy, we examine psychological factors that may boost or inhibit prioritization. We hypothesized that perceived social norms from people's own political party influence their climate policy prioritization beyond their personal belief in climate change. In Study 1, a large, diverse sample of Democratic and Republican participants (N = 887) reported their prioritization of climate policy relative to other issues. Participants' perceptions of their political ingroup's social norms about climate policy prioritization were the strongest predictor of personal climate policy prioritization-stronger even than participants' belief in climate change, political orientation, environmental identity, and environmental values. Perceptions of political outgroup norms did not predict prioritization. In Study 2 (N = 217), we experimentally manipulated Democratic and Republican descriptive norms of climate policy prioritization. Participants' prioritization of climate policy was highest when both the political ingroup and the outgroup prioritized climate policy. Ingroup norms had a strong influence on personal policy prioritization whereas outgroup norms did not. These findings demonstrate that, beyond personal beliefs and other individual differences, ingroup social norms shape the public's prioritization of climate change as a policy issue.Supplementary informationThe online version contains supplementary material available at 10.1007/s10584-022-03396-x.
| S-EPMC9289929 | biostudies-literature
Project description: Not available
| S-EPMC3977730 | biostudies-literature
Project description:INTRODUCTION:The rising popularity of social media, since their inception around 20 years ago, has been echoed in the growth of health-related research using data derived from them. This has created a demand for literature reviews to synthesise this emerging evidence base and inform future activities. Existing reviews tend to be narrow in scope, with limited consideration of the different types of data, analytical methods and ethical issues involved. There has also been a tendency for research to be siloed within different academic communities (eg, computer science, public health), hindering knowledge translation. To address these limitations, we will undertake a comprehensive scoping review, to systematically capture the broad corpus of published, health-related research based on social media data. Here, we present the review protocol and the pilot analyses used to inform it. METHODS:A version of Arksey and O'Malley's five-stage scoping review framework will be followed: (1) identifying the research question; (2) identifying the relevant literature; (3) selecting the studies; (4) charting the data and (5) collating, summarising and reporting the results. To inform the search strategy, we developed an inclusive list of keyword combinations related to social media, health and relevant methodologies. The frequency and variability of terms were charted over time and cross referenced with significant events, such as the advent of Twitter. Five leading health, informatics, business and cross-disciplinary databases will be searched: PubMed, Scopus, Association of Computer Machinery, Institute of Electrical and Electronics Engineers and Applied Social Sciences Index and Abstracts, alongside the Google search engine. There will be no restriction by date. ETHICS AND DISSEMINATION:The review focuses on published research in the public domain therefore no ethics approval is required. The completed review will be submitted for publication to a peer-reviewed, interdisciplinary open access journal, and conferences on public health and digital research.
| S-EPMC6303712 | biostudies-other