Conceptual model of comprehensive research metrics for improved human health and environment

Engel-Cox, Jill; Houten, Bennett Van; Phelps, Jerry; Rose, Shyanika W.

doi:10.1590/s1413-81232009000200020

Cited by 10 publications

(5 citation statements)

References 17 publications

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“…Evaluating performance in interdisciplinary biomedical research is highly complex, requiring a pluralistic approach that extends beyond conventional metrics 1. New approaches to evaluation link research activities to spatially and temporally remote outcomes through, for example, the systematic logic model of the National Institute of Environmental Health Sciences 2. The framework of the logic model clearly differentiates between academic outputs (e.g.…”

Section: Introductionmentioning

confidence: 99%

Evaluation metrics for biostatistical and epidemiological collaborations

Rubio

Junco

Bhore

et al. 2011

Statistics in Medicine

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Increasing demands for evidence-based medicine and for the translation of biomedical research into individual and public health benefit have been accompanied by the proliferation of special units that offer expertise in biostatistics, epidemiology, and research design (BERD) within academic health centers. Objective metrics that can be used to evaluate, track, and improve the performance of these BERD units are critical to their successful establishment and sustainable future. To develop a set of reliable but versatile metrics that can be adapted easily to different environments and evolving needs, we consulted with members of BERD units from the consortium of academic health centers funded by the Clinical and Translational Science Award Program of the National Institutes of Health. Through a systematic process of consensus building and document drafting, we formulated metrics that covered the three identified domains of BERD practices: the development and maintenance of collaborations with clinical and translational science investigators, the application of BERD-related methods to clinical and translational research, and the discovery of novel BERD-related methodologies. In this article, we describe the set of metrics and advocate their use for evaluating BERD practices. The routine application, comparison of findings across diverse BERD units, and ongoing refinement of the metrics will identify trends, facilitate meaningful changes, and ultimately enhance the contribution of BERD activities to biomedical research.

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Section: Introductionmentioning

confidence: 99%

Evaluation metrics for biostatistical and epidemiological collaborations

Rubio

Junco

Bhore

et al. 2011

Statistics in Medicine

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“…Source: Buxton and Hanney (1996) This framework is not a measurement approach in itself; rather, it provides the logical underpinnings for many other analyses (and indeed for many other frameworks adapted for use in specific contexts (CAHS, 2009;Banzi et al 2011;Engel-Cox et al 2008)). It has also been extended for applications outside of health (Klautzer et al 2011).…”

Section: Figure 5 Payback Logic Modelmentioning

confidence: 99%

Evidence synthesis on measuring the distribution of benefits of research and innovation

Guthrie¹,

d'Angelo²,

Ioppolo³

et al. 2018

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“…Technology Network(ATN, 2007) Engel-Cox et al (2008). used a similar approach for developing a conceptual model for research metrics.…”

mentioning

confidence: 99%

Measuring the benefits of university research: impact and the REF in the UK

Khazragui¹,

Hudson²

2014

Research Evaluation

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The Research Excellence Framework (REF) is the latest attempt by the UK government to evaluate research in UK universities. A key component of this is the evaluation of the economic and societal impact of research. We discuss the nature of such impact and how, in an ideal world we would measure it. We then evaluate a number of REF case studies and conclude that they are a long way from being an accurate reflection of impact. They are primarily narratives, with little hard information and no attempt to discount over time or spatially, or to evaluate against a counterfactual. But the REF deserves credit for focusing attention on impact and it must be recognised that at this point in time, a first best methodology is not possible. Both the research councils and the universities need to begin collecting data which will facilitate improved analyses in the future.

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Conceptual model of comprehensive research metrics for improved human health and environment

Cited by 10 publications

References 17 publications

Evaluation metrics for biostatistical and epidemiological collaborations

Evaluation metrics for biostatistical and epidemiological collaborations

Evidence synthesis on measuring the distribution of benefits of research and innovation

Measuring the benefits of university research: impact and the REF in the UK

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