Design-Based Approach for Analysing Survey Data in Veterinary Research.
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ABSTRACT:
SUBMITTER: Yang DA
PROVIDER: S-EPMC8227077 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundBias due to missing data is a major concern in electronic health record (EHR)-based research. As part of an ongoing EHR-based study of weight change among patients treated for depression, we conducted a survey to investigate determinants of missingness in the available weight information and to evaluate the missing-at-random assumption.MethodsWe identified 8,345 individuals enrolled in a large EHR-based health care system who had monotherapy treatment for depression from April 2008 to March 2010. A stratified sample of 1,153 individuals completed a detailed survey. Logistic regression was used to investigate determinants of whether a patient (1) had an opportunity to be weighed at treatment initiation (baseline), and (2) had a weight measurement recorded. Parallel analyses were conducted to investigate missingness during follow-up. Throughout, inverse-probability weighting was used to adjust for the design and survey nonresponse. Analyses were also conducted to investigate potential recall bias.ResultsMissingness at baseline and during follow-up was associated with numerous factors not routinely collected in the EHR including whether or not the patient had ever chosen not to be weighed, external weight control activities, and self-reported baseline weight. Patient attitudes about their weight and perceptions regarding the potential impact of their depression treatment on weight were not related to missingness.ConclusionAdopting a comprehensive strategy to investigate missingness early in the research process gives researchers information necessary to evaluate key assumptions. While the survey presented focuses on outcome data, the overarching strategy can be applied to any and all data elements subject to missingness.
| S-EPMC4666800 | biostudies-literature
Project description:ObjectiveThe study investigated veterinary medicine librarians' experience with and perceptions of research data services. Many academic libraries have begun to offer research data services in response to researchers' increased need for data management support. To date, such services have typically been generic, rather than discipline-specific, to appeal to a wide variety of researchers.MethodsAn online survey was deployed to identify trends regarding research data services in veterinary medicine libraries. Participants were identified from a list of contacts from the MLA Veterinary Medical Libraries Section.ResultsAlthough many respondents indicated that they have a professional interest in research data services, the majority of veterinary medicine librarians only rarely or occasionally provide data management support as part of their regular job responsibilities. There was little consensus as to whether research data services should be core to a library's mission despite their perceived importance to the advancement of veterinary research. Furthermore, most respondents stated that research data services are just as or somewhat less important than the other services that they provide and feel only slightly or somewhat prepared to offer such services.ConclusionsLacking a standard definition of "research data" and a common understanding of precisely what research data services encompass, it is difficult for veterinary medicine librarians and libraries to define and understand their roles in research data services. Nonetheless, they appear to have an interest in learning more about and providing research data services.
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Project description:With Open Data becoming more popular and more public bodies publishing their datasets, the need for educating prospective graduates on how they can use them has become prominent. This study examines the use of the Problem Based Learning (PBL) method and educational technologies to support the development of Open Data skills in university students. The study follows a Design Based Research approach and consists of three phases: a) examination of stakeholders' needs, b) design of an Open Data module, and c) re-design of the module based on the outcomes of its first run. The data collected throughout the three phases come from various sources, namely interviews with practitioners, focus groups with students, and tutors' reflection. The findings suggest that while the PBL method is suitable for Open Data education, special care should be taken to ensure that the potential of educational technologies is fully realised. The study concludes with design principles that aim to guide instructors on how they can incorporate the PBL method and digital tools into Open Data education effectively.Supplementary informationThe online version contains supplementary material available at 10.1007/s10639-022-10995-9.
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Project description:This article presents analyses of data from a pilot study in Nepal that was designed to provide an initial examination of the errors and costs associated with an innovative methodology for survey data collection. We embedded a randomized experiment within a long-standing panel survey, collecting data on a small number of items with varying sensitivity from a probability sample of 450 young Nepalese adults. Survey items ranged from simple demographics to indicators of substance abuse and mental health problems. Sampled adults were randomly assigned to one of three different modes of data collection: 1) a standard one-time telephone interview, 2) a "single sitting" back-and-forth interview with an interviewer using text messaging, and 3) an interview using text messages within a modular design framework (which generally involves breaking the survey response task into distinct parts over a short period of time). Respondents in the modular group were asked to respond (via text message exchanges with an interviewer) to only one question on a given day, rather than complete the entire survey. Both bivariate and multivariate analyses demonstrate that the two text messaging modes increased the probability of disclosing sensitive information relative to the telephone mode, and that respondents in the modular design group, while responding less frequently, found the survey to be significantly easier. Further, those who responded in the modular group were not unique in terms of available covariates, suggesting that the reduced item response rates only introduced limited nonresponse bias. Future research should consider enhancing this methodology, applying it with other modes of data collection (e. g., web surveys), and continuously evaluating its effectiveness from a total survey error perspective.
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Project description:BackgroundCreative arts practice can enhance the depth and quality of mental health research by capturing and foregrounding participants' lived experience. Creative methods are emotionally activating and promote multiple perspectives, tolerating ambiguities and uncertainties, which are shared and even celebrated.Key argumentsMethods such as photovoice use imagery to elucidate narratives that are not easily captured by more traditional interview-based research techniques. However, the use of creative methods and participatory research remains novel as there is little guidance of how to navigate conceptual, practical, and analytical challenges.ConclusionThis paper considers these challenges, and puts forward practical and theory informed recommendations, using as study of photovoice methods for investigating ethnic inequalities in the use of the mental health act (Co-Pact) as a case study.
| S-EPMC10111907 | biostudies-literature
Project description:Progress in MS-based methods for veterinary research and diagnostics is lagging behind compared to the human research, and proteome data of domestic animals is still not well represented in open source data repositories. This is particularly true for the equine species. Here we present a first Equine PeptideAtlas encompassing high-resolution tandem MS analyses of 51 samples representing a selection of equine tissues and body fluids from healthy and diseased animals. The raw data were processed through the Trans-Proteomic Pipeline to yield high quality identification of proteins and peptides. The current release comprises 24 131 distinct peptides representing 2636 canonical proteins observed at false discovery rates of 0.2% at the peptide level and 1.4% at the protein level. Data from the Equine PeptideAtlas are available for experimental planning, validation of new datasets, and as a proteomic data mining resource. The advantages of the Equine PeptideAtlas are demonstrated by examples of mining the contents for information on potential and well-known equine acute phase proteins, which have extensive general interest in the veterinary clinic. The extracted information will support further analyses, and emphasizes the value of the Equine PeptideAtlas as a resource for the design of targeted quantitative proteomic studies.
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Project description:Large-scale genetic and genomic data are increasingly available and the major bottleneck in their analysis is a lack of sufficiently scalable computational tools. To address this problem in the context of complex traits analysis, we present DISSECT. DISSECT is a new and freely available software that is able to exploit the distributed-memory parallel computational architectures of compute clusters, to perform a wide range of genomic and epidemiologic analyses, which currently can only be carried out on reduced sample sizes or under restricted conditions. We demonstrate the usefulness of our new tool by addressing the challenge of predicting phenotypes from genotype data in human populations using mixed-linear model analysis. We analyse simulated traits from 470,000 individuals genotyped for 590,004 SNPs in ?4?h using the combined computational power of 8,400 processor cores. We find that prediction accuracies in excess of 80% of the theoretical maximum could be achieved with large sample sizes.
| S-EPMC4682108 | biostudies-literature
Project description:Design disciplines have a long history of creating well-integrated solutions to challenges which are complex, uncertain and contested by multiple stakeholders. Society faces similar challenges in implementing the Sustainable Development Goals, so design methods hold much potential. While principles of good design are well established, there has been limited integration of design thinking with sustainability science. To advance this integration, we examine the process of designing MetaMAP: an interactive graphic tool for collaborating to understand social-ecological systems and design well-integrated solutions. MetaMAP was created using Research through Design methods which integrate creative and scientific thinking. By applying design thinking, researchers and practitioners from different backgrounds undertook multiple cycles of problem framing, solution development, testing and reflection. The testing was highly collaborative involving over 150 people from diverse disciplines in workshops, case studies, interviews and critique. Reflecting on this process, we discuss design principles and opportunities for integrating design thinking with sustainability science to help achieve Sustainable Development Goals.
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Project description:High-quality research demonstrating a lack of effectiveness may facilitate the 'de-adoption' of ineffective health services. However, there has been little debate on the optimal design for ineffectiveness research-studies exploring the research hypothesis that an intervention is ineffective. The aim of this study was to explore investigators' preferences for trial design for ineffectiveness research. We conducted a mixed-methods online survey with principle investigators identified from clinicaltrials.gov. A vignette described researchers planning a trial to test a widely used intervention they hypothesised was ineffective. One multiple-choice question asked whether a superiority trial or equivalence trial design was favoured, and one free-response question asked about the reasons for that choice. Free-response answers were analysed using content analysis to identify related reasons. 139 participants completed the survey (completion rate 37.5%). Overall, 56.8% favoured superiority trials, 27.3% favoured equivalence trials and 15.8% were unsure. Reasons identified for favouring superiority trials were: (1) evidence of superiority should be required to justify active treatment, (2) superiority trials are more familiar, (3) placebo should not be the comparator in equivalence trials and (4) superiority trials require smaller sample sizes. Reasons identified for favouring equivalence trials were: (1) negative superiority trials represent a lack of evidence of effectiveness, not evidence of ineffectiveness and (2) the research hypothesis should not be the same as the null hypothesis. A minority of experienced researchers favour equivalence trials for ineffectiveness research, and misconceptions and lack of familiarity with equivalence trials may be contributing factors.
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Project description:Ethnobotanical databases serve as repositories of traditional knowledge (TK), either at international or local scales. By documenting plant species with traditional use, and most importantly, the applications and modes of use of such species, ethnobotanical databases play a role in the conservation of TK and also provide access to information that could improve hypothesis generation and testing in ethnobotanical studies. Brazil has a rich medicinal flora and a rich cultural landscape. Nevertheless, cultural change and ecological degradation can lead to loss of TK. Here, we present an online database developed with open-source tools with a capacity to include all medicinal flora of Brazil. We present test data for the Leguminosae comprising a total of 2078 records, referred to here as use reports, including data compiled from literature and herbarium sources. Unlike existing databases, Ewé provides tools for the visualization of large datasets, facilitating hypothesis generation and meta-analyses. The Ewé database is currently available at www.ewedb.com.
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