Education and service : how theories can help in understanding tensions

Acknowledgements: our thanks to Ayelet Kuper for a helpfuldiscussion about the possible application of discourseanalysis to service–training tensionsPeer reviewedPostprin

‘Everyone can imagine their own Gellert’: the democratic artist and ‘inclusion’ in primary and nursery classrooms

What do artists do when they work in schools? Can teachers do the same? These were the questions at the heart of our recent research, investigating the work of 12 artists working in primary and secondary schools in England. Funded by Creativity, Culture and Education as a ‘legacy’ project of Creative Partnerships (2003–2011) our intention was to develop a theorisation of artists’ practice that could inform the work that teachers do. In this paper, we report on a key aspect of the Signature Pedagogies project (www.signaturepedagogies.org.uk) the way in which artists approached the issue of inclusion. Through an examination of the work of three story-makers in primary and nursery schools, documented through observation, film and interview, we show that the democratic participatory practices they adopted were based on a fundamental belief that: every child was capable of having ideas; every child could contribute meaningfully to discussions; and every child was integral to a collective ‘performance’. We conclude that these artists’ democratic orientations may well be difficult for teachers to adopt in the current moment, but that this artistic work in schools may still provide a welcome relief for all involved, as well as maintaining an exemplar of alternative pedagogical practice that might be expanded in a changed policy environment

Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery.

Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies

Critical conversations: Tensions and opportunities of the dialogical classroom

English teachers and educators of English teachers should work within rather than against the tensions present in their classrooms. For us, nothing could be more key. Until university teacher educators construct and enact classrooms that embrace the dialogical tensions and possibilities within those settings, new and veteran teachers in the profession will have few if any sustained experiences upon which to base their own dialogical classrooms. Moreover, to either deny that tensions exist or to struggle to eradicate them is to misunderstand the purpose and possibility of tension. Learners caught between stabilizing and destabilizing tensions enter a state of wobble, one that asks them to pay attention to the issues at hand and to author a response. The goal is not to remove oneself from that tension but instead to enter into a dialogue that, like the cables on a suspension bridge, uses tension for support and equilibrium

Open Application of Statistical and Machine Learning Models to Explore the Impact of Environmental Exposures on Health and Disease: An Asthma Use Case

ICEES (Integrated Clinical and Environmental Exposures Service) provides a disease-agnostic, regulatory-compliant approach for openly exposing and analyzing clinical data that have been integrated at the patient level with environmental exposures data. ICEES is equipped with basic features to support exploratory analysis using statistical approaches, such as bivariate chi-square tests. We recently developed a method for using ICEES to generate multivariate tables for subsequent application of machine learning and statistical models. The objective of the present study was to use this approach to identify predictors of asthma exacerbations through the application of three multivariate methods: conditional random forest, conditional tree, and generalized linear model. Among seven potential predictor variables, we found five to be of significant importance using both conditional random forest and conditional tree: prednisone, race, airborne particulate exposure, obesity, and sex. The conditional tree method additionally identified several significant two-way and three-way interactions among the same variables. When we applied a generalized linear model, we identified four significant predictor variables, namely prednisone, race, airborne particulate exposure, and obesity. When ranked in order by effect size, the results were in agreement with the results from the conditional random forest and conditional tree methods as well as the published literature. Our results suggest that the open multivariate analytic capabilities provided by ICEES are valid in the context of an asthma use case and likely will have broad value in advancing open research in environmental and public health

Development and Application of an Open Tool for Sharing and Analyzing Integrated Clinical and Environmental Exposures Data: Asthma Use Case

BackgroundThe Integrated Clinical and Environmental Exposures Service (ICEES) serves as an open-source, disease-agnostic, regulatory-compliant framework and approach for openly exposing and exploring clinical data that have been integrated at the patient level with a variety of environmental exposures data. ICEES is equipped with tools to support basic statistical exploration of the integrated data in a completely open manner. ObjectiveThis study aims to further develop and apply ICEES as a novel tool for openly exposing and exploring integrated clinical and environmental data. We focus on an asthma use case. MethodsWe queried the ICEES open application programming interface (OpenAPI) using a functionality that supports chi-square tests between feature variables and a primary outcome measure, with a Bonferroni correction for multiple comparisons (α=.001). We focused on 2 primary outcomes that are indicative of asthma exacerbations: annual emergency department (ED) or inpatient visits for respiratory issues; and annual prescriptions for prednisone. ResultsOf the 157,410 patients within the asthma cohort, 26,332 (16.73%) had 1 or more annual ED or inpatient visits for respiratory issues, and 17,056 (10.84%) had 1 or more annual prescriptions for prednisone. We found that close proximity to a major roadway or highway, exposure to high levels of particulate matter ≤2.5 μm (PM2.5) or ozone, female sex, Caucasian race, low residential density, lack of health insurance, and low household income were significantly associated with asthma exacerbations (P<.001). Asthma exacerbations did not vary by rural versus urban residence. Moreover, the results were largely consistent across outcome measures. ConclusionsOur results demonstrate that the open-source ICEES can be used to replicate and extend published findings on factors that influence asthma exacerbations. As a disease-agnostic, open-source approach for integrating, exposing, and exploring patient-level clinical and environmental exposures data, we believe that ICEES will have broad adoption by other institutions and application in environmental health and other biomedical fields