MITEP: REFLECTIVE JOURNEY

This research looks at the use of the Interactive Student Notebook (ISN) in the math classroom and the impact on student achievement as part of the MiTEP program. A reflective critical analysis of the MiTEP program discusses impact on teacher pedagogy, leadership, and connections to people and resources. The purpose of the study stemmed from the lack of student retention, poor organizational skills, and the students’ inability to demonstrate college readiness skills such as how to study, completing homework, and thinking independently. Motivation also stemmed from teacher frustration. The research was conducted at Linden Grove Middle School in Kalamazoo Michigan in a strategic math class. Twenty-two sixth graders, thirty-two seventh graders, and forty eighth graders were part of the study.Students were given the Strategic Math Inventory (SMI) test in week 1 of the class and again at the end of a 12 week marking period. Students participated in an attitude survey to record their feelings about the use of the ISN in the strategic math classroom. The data compared the control group (the previous year’s [2012-2013] growth data) to the experimental group, the current year’s (2013-2014) growth data. Both groups were statistically similar in that the mean average was about a 4th grade level equivalency and the groups had similar numbers of grade level students. The significant findings were in the amount of growth made using the ISN. The control group started with a mean average of 586.6 and ended with a mean average of 697.1, making about one year’s growth from a 4th to a 5th grade level equivalency. The experimental group started with a mean average of 585.2 and ended with a mean average of 744.2, making about two years growth from a 4th to a 6th grade level equivalency. This is double the growth of the control group. The Cohen’s test resulted in a score of 0.311 which describes that the teaching method, the use of the ISN in the math classroom had a medium impact on student growth.

Radiometric Method for Emissivity Retrieval in High Reflective Materials

High reflective materials in the microwave region play a very important role in the realization of antenna reflectors for a broad range of applications, including radiometry. These reflectors have a characteristic emissivity which needs to be characterized accurately in order to perform a correct radiometric calibration of the instrument. Such a characterization can be performed by using open resonators, waveguide cavities or by radiometric measurements. The latter consists of comparative radiometric observations of absorbers, reference mirrors and the sample under test, or using the cold sky radiation as a direct reference source. While the first two mentioned techniques are suitable for the characterization of metal plates and mirrors, the latter has the advantages to be also applicable to soft materials. This paper describes how, through this radiometric techniques, it is possible to characterize the emissivity of the sample relative to a reference mirror and how to characterize the absolute emissivity of the latter by performing measurements at different incident angles. The results presented in this paper are based on our investigations on emissivity of a multilayer insulation material (MLI) for space mission, at the frequencies of 22 and 90 GHz.

Reconstructing Arguments: Formalization and Reflective Equilibrium

Traditional logical reconstruction of arguments aims at assessing the validity of ordinary language arguments. It involves several tasks: extracting argumentations from texts, breaking up complex argumentations into individual arguments, framing arguments in standard form, as well as formalizing arguments and showing their validity with the help of a logical formalism. These tasks are guided by a multitude of partly antagonistic goals, they interact in various feedback loops, and they are intertwined with the development of theories of valid inference and adequate formalization. This paper explores how the method of reflective equilibrium can be used for modelling the complexity of such reconstructions and for justifying the various steps involved. The proposed approach is illustrated and tested in a detailed reconstruction of the beginning of Anselm’s De casu diaboli.

Intra-Rater and Inter-Rater Reliability of a Medical Record Abstraction Study on Transition of Care after Childhood Cancer.

BACKGROUND The abstraction of data from medical records is a widespread practice in epidemiological research. However, studies using this means of data collection rarely report reliability. Within the Transition after Childhood Cancer Study (TaCC) which is based on a medical record abstraction, we conducted a second independent abstraction of data with the aim to assess a) intra-rater reliability of one rater at two time points; b) the possible learning effects between these two time points compared to a gold-standard; and c) inter-rater reliability. METHOD Within the TaCC study we conducted a systematic medical record abstraction in the 9 Swiss clinics with pediatric oncology wards. In a second phase we selected a subsample of medical records in 3 clinics to conduct a second independent abstraction. We then assessed intra-rater reliability at two time points, the learning effect over time (comparing each rater at two time-points with a gold-standard) and the inter-rater reliability of a selected number of variables. We calculated percentage agreement and Cohen's kappa. FINDINGS For the assessment of the intra-rater reliability we included 154 records (80 for rater 1; 74 for rater 2). For the inter-rater reliability we could include 70 records. Intra-rater reliability was substantial to excellent (Cohen's kappa 0-6-0.8) with an observed percentage agreement of 75%-95%. In all variables learning effects were observed. Inter-rater reliability was substantial to excellent (Cohen's kappa 0.70-0.83) with high agreement ranging from 86% to 100%. CONCLUSIONS Our study showed that data abstracted from medical records are reliable. Investigating intra-rater and inter-rater reliability can give confidence to draw conclusions from the abstracted data and increase data quality by minimizing systematic errors.

Building the capacity for learning and change through reflective conversation

Conversation is central to the process of organizational learning and change. Drawing on the notion of reflective conversation, we describe an action research project, "learning through listening" in Omega, a residential healthcare organization. In this project, service users, staff, members of management committees, trustees, managers, and central office staff participated in listening to each other and in working together towards building capacity for creating their own vision of how the organization could move into the future, according to its values and ethos. In doing so they developed ways of engaging in reflective conversation that enabled progress towards a strategic direction.

Data Accuracy in Medical Record Abstraction

Clinical Research Data Quality Literature Review and Pooled Analysis We present a literature review and secondary analysis of data accuracy in clinical research and related secondary data uses. A total of 93 papers meeting our inclusion criteria were categorized according to the data processing methods. Quantitative data accuracy information was abstracted from the articles and pooled. Our analysis demonstrates that the accuracy associated with data processing methods varies widely, with error rates ranging from 2 errors per 10,000 files to 5019 errors per 10,000 fields. Medical record abstraction was associated with the highest error rates (70–5019 errors per 10,000 fields). Data entered and processed at healthcare facilities had comparable error rates to data processed at central data processing centers. Error rates for data processed with single entry in the presence of on-screen checks were comparable to double entered data. While data processing and cleaning methods may explain a significant amount of the variability in data accuracy, additional factors not resolvable here likely exist. Defining Data Quality for Clinical Research: A Concept Analysis Despite notable previous attempts by experts to define data quality, the concept remains ambiguous and subject to the vagaries of natural language. This current lack of clarity continues to hamper research related to data quality issues. We present a formal concept analysis of data quality, which builds on and synthesizes previously published work. We further posit that discipline-level specificity may be required to achieve the desired definitional clarity. To this end, we combine work from the clinical research domain with findings from the general data quality literature to produce a discipline-specific definition and operationalization for data quality in clinical research. While the results are helpful to clinical research, the methodology of concept analysis may be useful in other fields to clarify data quality attributes and to achieve operational definitions. Medical Record Abstractor’s Perceptions of Factors Impacting the Accuracy of Abstracted Data Medical record abstraction (MRA) is known to be a significant source of data errors in secondary data uses. Factors impacting the accuracy of abstracted data are not reported consistently in the literature. Two Delphi processes were conducted with experienced medical record abstractors to assess abstractor’s perceptions about the factors. The Delphi process identified 9 factors that were not found in the literature, and differed with the literature by 5 factors in the top 25%. The Delphi results refuted seven factors reported in the literature as impacting the quality of abstracted data. The results provide insight into and indicate content validity of a significant number of the factors reported in the literature. Further, the results indicate general consistency between the perceptions of clinical research medical record abstractors and registry and quality improvement abstractors. Distributed Cognition Artifacts on Clinical Research Data Collection Forms Medical record abstraction, a primary mode of data collection in secondary data use, is associated with high error rates. Distributed cognition in medical record abstraction has not been studied as a possible explanation for abstraction errors. We employed the theory of distributed representation and representational analysis to systematically evaluate cognitive demands in medical record abstraction and the extent of external cognitive support employed in a sample of clinical research data collection forms. We show that the cognitive load required for abstraction in 61% of the sampled data elements was high, exceedingly so in 9%. Further, the data collection forms did not support external cognition for the most complex data elements. High working memory demands are a possible explanation for the association of data errors with data elements requiring abstractor interpretation, comparison, mapping or calculation. The representational analysis used here can be used to identify data elements with high cognitive demands.