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From Theory to Practice: Gathering Evidence for the Validity of Data Collected with the Interdisciplinary Science Rubric (IDSR).

ABSTRACT: In a world of burgeoning societal issues, future scientists must be equipped to work interdisciplinarily to address real-world problems. To train undergraduate students toward this end, practitioners must also have quality assessment tools to measure students' ability to think within an interdisciplinary system. There is, however, a dearth of instruments that accurately measure this competency. Using a theoretically and empirically based model, we developed an instrument, the Interdisciplinary Science Rubric (IDSR), to measure undergraduate students' interdisciplinary science thinking. An essay assignment was administered to 102 students across five courses at three different institutions. Students' work was scored with the newly developed rubric. Evidence of construct validity was established through novice and expert response processes via semistructured, think-aloud interviews with 29 students and four instructors to ensure the constructs and criteria within the instrument were operating as intended. Interrater reliability of essay scores was collected with the instructors of record (κ = 0.67). An expert panel of discipline-based education researchers (n = 11) were consulted to further refine the scoring metric of the rubric. Results indicate that the IDSR produces valid data to measure undergraduate students' ability to think interdisciplinarily in science.

SUBMITTER: Tripp B

PROVIDER: S-EPMC8711832 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAlthough evolutionary theory is foundational and integrative in modern biology, there remains widespread lack of acceptance among U.S. residents. An interdisciplinary approach to teaching evolutionary theory at the undergraduate level has many advantages, such as giving students a context for learning about evolution and application of evolutionary theory to other academic disciplines and everyday life. While there are foundational examples of interdisciplinary approaches to teaching evolutionary theory, there are few examples of courses with application of evolutionary theory to issues of sustainability, such as conservation or global climate change. We build on the practical and theoretical work of others to create an interdisciplinary course on evolutionary theory for non-science majors, with ties to sustainability. Our course is taught in three modules, with extensive readings and hands-on lab activities. The first module is focused on honey bee biology, with hands-on beekeeping experiences; the second module on native plants and community education on sustainability; and the third module on the evolution of the subjective human experience of free will.ResultsWe found that students in our course experienced an increased acceptance of evolutionary theory. We found that students also met the course leaning objectives, of basic knowledge of evolutionary theory and application of evolutionary theory to other disciplines, assessed through group and individual major assignments. We also found that students had an expanded perspective on interdisciplinary application of evolutionary theory, assessed through closed-ended survey questions and analysis of open-ended writing.ConclusionsStudents in our course experienced an increase of acceptance of evolutionary theory and an expanded perspective on interdisciplinary application of evolutionary theory, despite the fact that many students were not science majors.Supplementary informationThe online version contains supplementary material available at 10.1186/s12052-023-00188-4.

Project description:ObjectivesTo investigate the validity of data extraction in systematic reviews of adverse events, the effect of data extraction errors on the results, and to develop a classification framework for data extraction errors to support further methodological research.DesignReproducibility study.Data sourcesPubMed was searched for eligible systematic reviews published between 1 January 2015 and 1 January 2020. Metadata from the randomised controlled trials were extracted from the systematic reviews by four authors. The original data sources (eg, full text and ClinicalTrials.gov) were then referred to by the same authors to reproduce the data used in these meta-analyses.Eligibility criteria for selecting studiesSystematic reviews were included when based on randomised controlled trials for healthcare interventions that reported safety as the exclusive outcome, with at least one pair meta-analysis that included five or more randomised controlled trials and with a 2×2 table of data for event counts and sample sizes in intervention and control arms available for each trial in the meta-analysis.Main outcome measuresThe primary outcome was data extraction errors summarised at three levels: study level, meta-analysis level, and systematic review level. The potential effect of such errors on the results was further investigated.Results201 systematic reviews and 829 pairwise meta-analyses involving 10 386 randomised controlled trials were included. Data extraction could not be reproduced in 1762 (17.0%) of 10 386 trials. In 554 (66.8%) of 829 meta-analyses, at least one randomised controlled trial had data extraction errors; 171 (85.1%) of 201 systematic reviews had at least one meta-analysis with data extraction errors. The most common types of data extraction errors were numerical errors (49.2%, 867/1762) and ambiguous errors (29.9%, 526/1762), mainly caused by ambiguous definitions of the outcomes. These categories were followed by three others: zero assumption errors, misidentification, and mismatching errors. The impact of these errors were analysed on 288 meta-analyses. Data extraction errors led to 10 (3.5%) of 288 meta-analyses changing the direction of the effect and 19 (6.6%) of 288 meta-analyses changing the significance of the P value. Meta-analyses that had two or more different types of errors were more susceptible to these changes than those with only one type of error (for moderate changes, 11 (28.2%) of 39 v 26 (10.4%) 249, P=0.002; for large changes, 5 (12.8%) of 39 v 8 (3.2%) of 249, P=0.01).ConclusionSystematic reviews of adverse events potentially have serious issues in terms of the reproducibility of the data extraction, and these errors can mislead the conclusions. Implementation guidelines are urgently required to help authors of future systematic reviews improve the validity of data extraction.

Project description:Partnering with people most affected by mental health problems can transform mental health outcomes. Citizen science as a research approach enables partnering with the public at a substantial scale, but there is scarce guidance on its use in mental health research. To develop best practise guidelines for conducting and reporting research, we conducted a systematic review of studies reporting mental health citizen science research. Documents were identified from electronic databases (n = 10), grey literature, conference proceedings, hand searching of specific journals and citation tracking. Document content was organised in NVIVO using the ten European Citizen Science Association (ECSA) citizen science principles. Best practise guidelines were developed by (a) identifying approaches specific to mental health research or where citizen science and mental health practises differ, (b) identifying relevant published reporting guidelines and methodologies already used in mental health research, and (c) identifying specific elements to include in reporting studies. A total of 14,063 documents were screened. Nine studies were included, from Australia, Belgium, Canada, Denmark, Netherlands, Spain, the UK, and the United States. Citizen scientists with lived experience of mental health problems were involved in data collection, analysis, project design, leadership, and dissemination of results. Most studies reported against some ECSA principles but reporting against these principles was often unclear and unstated. Best practise guidelines were developed, which identified mental health-specific issues relevant to citizen science, and reporting recommendations. These included citizen science as a mechanism for empowering people affected by mental health problems, attending to safeguarding issues such as health-related advice being shared between contributors, the use of existing health research reporting guidelines, evaluating the benefits for contributors and impact on researchers, explicit reporting of participation at each research stage, naming the citizen science platform and data repository, and clear reporting of consent processes, data ownership, and data sharing arrangements. We conclude that citizen science is feasible in mental health and can be complementary to other participatory approaches. It can contribute to active involvement, engagement, and knowledge production with the public. The proposed guidelines will support the quality of citizen science reporting.

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From Theory to Practice: Gathering Evidence for the Validity of Data Collected with the Interdisciplinary Science Rubric (IDSR).

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