Include medical ethics in the Research Excellence Framework

The Research Excellence Framework of the Higher Education Funding Council for England is taking place in 2013, its three key elements being outputs (65% of the profile), impact (20%), and “quality of the research environment” (15%). Impact will be assessed using case studies that “may include any social, economic or cultural impact or benefit beyond academia that has taken place during the assessment period.”1 Medical ethics in the UK still does not have its own cognate assessment panel—for example, bioethics or applied ethics—unlike in, for example, Australia. Several researchers in medical ethics have reported to the Institute of Medical Ethics that during the internal preliminary stage of the Research Excellence Framework several medical schools have decided to include only research that entails empirical data gathering. Thus, conceptual papers and ethical analysis will be excluded. The arbitrary exclusion of reasoned discussion of medical ethics issues as a proper subject for medical research unless it is based on empirical data gathering is conceptually mistaken. “Empirical ethics” is, of course, a legitimate component of medical ethics research, but to act as though it is the only legitimate component suggests, at best, a partial understanding of the nature of ethics in general and medical ethics in particular. It also mistakenly places medicine firmly on only one side of the science/humanities “two cultures” divide instead of in its rightful place bridging the divide. Given the emphasis by the General Medical Council on medical ethics in properly preparing “tomorrow’s doctors,” we urge medical schools to find a way of using the upcoming Research Excellence Framework to highlight the expertise residing in their ethicist colleagues. We are confident that appropriate assessment will reveal work of high quality that can be shown to have social and cultural impact and benefit beyond academia, as required by the framework

Evaluating the effectiveness of data quality framework in software engineering

The quality of data is important in research working with data sets because poor data quality may lead to invalid results. Data sets contain measurements that are associated with metrics and entities; however, in some data sets, it is not always clear which entities have been measured and exactly which metrics have been used. This means that measurements could be misinterpreted. In this study, we develop a framework for data quality assessment that determines whether a data set has sufficient information to support the correct interpretation of data for analysis in empirical research. The framework incorporates a dataset metamodel and a quality assessment process to evaluate the data set quality. To evaluate the effectiveness of our framework, we conducted a user study. We used observations, a questionnaire and think aloud approach to provide insights into the framework through participant thought processes while applying the framework. The results of our study provide evidence that most participants successfully applied the definitions of dataset category elements and the formal definitions of data quality issues to the datasets. Further work is needed to reproduce our results with more participants, and to determine whether the data quality framework is generalizable to other types of data sets

Framework for sustainable TVET-Teacher Education Program in Malaysia Public Universities

Studies had stated that less attention was given to the education aspect, such as teaching and learning in planning for improving the TVET system. Due to the 21st Century context, the current paradigm of teaching for the TVET educators also has been reported to be fatal and need to be shifted. All these disadvantages reported hindering the country from achieving the 5th strategy in the Strategic Plan for Vocational Education Transformation to transform TVET system as a whole. Therefore, this study aims to develop a framework for sustainable TVET Teacher Education program in Malaysia. This study had adopted an Exploratory Sequential Mix-Method design, which involves a semi-structured interview (phase one) and survey method (phase two). Nine experts had involved in phase one chosen by using Purposive Sampling Technique. As in phase two, 118 TVET-TE program lecturers were selected as the survey sample chosen through random sampling method. After data analysis in phase one (thematic analysis) and phase two (Principal Component Analysis), eight domains and 22 elements have been identified for the framework for sustainable TVET-TE program in Malaysia. This framework was identified to embed the elements of 21st Century Education, thus filling the gap in this research. The research findings also indicate that the developed framework was unidimensional and valid for the development and research regarding TVET-TE program in Malaysia. Lastly, it is in the hope that this research can be a guide for the nations in producing a quality TVET teacher in the future

QUALITATIVE APPROACH IN POPULATION STUDY RESEARCH

This article addresses the assessment of data quality in qualitative research. The analysis shows that the use of the qualitative approach in demographic research has a selective approach in terms of topics. It is used to increase the information about a certain problem or to clarify the existing knowledge obtained from the quantitative approach. The criteria for evaluating the quality of qualitative data are in the context of the methodological aspects in the research, but still there is no agreement regarding the framework for evaluating the quality of the data. In perspectives, aspects suggested for assessing data quality are data collection method, sample, selection and training of interviewers, field work and data analysis

Guidelines for Data Acquisition, Quality and Curation for Observational Research Designs (DAQCORD).

BACKGROUND: High-quality data are critical to the entire scientific enterprise, yet the complexity and effort involved in data curation are vastly under-appreciated. This is especially true for large observational, clinical studies because of the amount of multimodal data that is captured and the opportunity for addressing numerous research questions through analysis, either alone or in combination with other data sets. However, a lack of details concerning data curation methods can result in unresolved questions about the robustness of the data, its utility for addressing specific research questions or hypotheses and how to interpret the results. We aimed to develop a framework for the design, documentation and reporting of data curation methods in order to advance the scientific rigour, reproducibility and analysis of the data. METHODS: Forty-six experts participated in a modified Delphi process to reach consensus on indicators of data curation that could be used in the design and reporting of studies. RESULTS: We identified 46 indicators that are applicable to the design, training/testing, run time and post-collection phases of studies. CONCLUSION: The Data Acquisition, Quality and Curation for Observational Research Designs (DAQCORD) Guidelines are the first comprehensive set of data quality indicators for large observational studies. They were developed around the needs of neuroscience projects, but we believe they are relevant and generalisable, in whole or in part, to other fields of health research, and also to smaller observational studies and preclinical research. The DAQCORD Guidelines provide a framework for achieving high-quality data; a cornerstone of health research