Grid-Enabled Measures

Moser, Richard P.; Hesse, Bradford W.; Shaikh, Anees; Courtney, Paul; Morgan, Glen D.; Augustson, Erik; Kobrin, Sarah C.; Levin, Kerry; Helba, Cynthia; Garner, David; Dunn, Marsha; Coa, Kisha

doi:10.1016/j.amepre.2011.01.004

Cited by 35 publications

(23 citation statements)

References 35 publications

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“…To help bring standardization to the field, a new resource is available for formal and informal reporting on measure constructs and instruments through the National Cancer Institute's Grid Enabled Measures shared decision making database. Ideally, this effort will support researchers in selecting strong measures and enable more complete reporting (15). …”

Section: Discussionmentioning

confidence: 99%

“It’s Valid and Reliable” Is Not Enough

et al. 2014

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Background This review systematically appraises the quality of reporting of measures used in trials to evaluate the effectiveness of patient decision aids (PtDAs) and presents recommendations for minimum reporting standards. Methods We reviewed measures of decision quality and decision process in 86 randomized controlled trials (RCTs) from the 2011 Cochrane Collaboration systematic review of PtDAs. Data on development of the measures, reliability, validity, responsiveness, precision, interpretability, feasibility and acceptability were independently abstracted by two reviewers. Results Information from 178 instances of use of measures was abstracted. Very few studies reported data on the performance of measures, with reliability (21%) and validity (16%) being the most common. Studies using new measures were less likely to include information about their psychometric performance. Limitations The review was limited to reporting of measures in studies included in the Cochrane review and did not consult prior publications. Conclusion There is very little reported about the development or performance of measures used to evaluate the effectiveness of PtDAs in published trials. Minimum reporting standards are proposed to enable authors to prepare study reports, editors and reviewers to evaluate submitted papers, and readers to appraise published studies.

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Section: Discussionmentioning

confidence: 99%

“It’s Valid and Reliable” Is Not Enough

et al. 2014

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“…Successful data harmonization requires both consensus measure development and co-calibrated measures. It requires research infrastructure, such as the NCI Grid-Enabled Measures portal (GEM), 94 to support sharing of measures and their attributes.…”

Section: Elucidating the Levels Of Influencementioning

confidence: 99%

Behavioral Research in Cancer Prevention and Control

Klein

Bloch

Hesse

et al. 2014

American Journal of Preventive Medicine

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Human behavior is central to the etiology and management of cancer outcomes and presents several avenues for targeted and sustained intervention. Psychosocial experiences such as stress and health behaviors including tobacco use, sun exposure, poor diet, and a sedentary lifestyle increase the risk of some cancers yet are often quite resistant to change. Cancer screening and other health services are misunderstood and over-utilized, and vaccination underutilized, in part because of the avalanche of information about cancer prevention. Coordination of cancer care is suboptimal, and only a small fraction of cancer patients enroll in clinical trials essential to the development of new cancer treatments. A growing population of cancer survivors has necessitated a fresh view of cancer as a chronic rather than acute disease. Fortunately, behavioral research can address a wide variety of key processes and outcomes across the cancer controbiol continuum from prevention to end-of-life care. Here we consider effects at the biobehavioral and psychological, social and organizational, and environmental levels. We challenge the research community to address key behavioral targets across all levels of influence, while taking into account the many new methodological tools that can facilitate this important work.

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“…Items have been used in several state surveys including a concentration among Appalachian states, and in a special adaptation of respondent-driven sampling in the territory of Guam. The modes in which questions have been administered have also varied, with a telephone-based random digit dial frame utilized in 2003 and 2005; a mixed telephone/postal frame in 2007–2008; postal only frame used from 2012 to 2015; and a proposal to translate items into American Sign Language (Finney Rutten et al 2012, 2010; Moser et al 2011; Nelson et al 2004; Tortolero-Luna et al 2010). …”

Section: Integrating Data Streamsmentioning

confidence: 99%

From Big Data to Knowledge in the Social Sciences

Hesse¹,

Moser²,

Riley³

2015

The ANNALS of the American Academy of Political and Social Scie

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One of the challenges associated with high-volume, diverse datasets is whether synthesis of open data streams can translate into actionable knowledge. Recognizing that challenge and other issues related to these types of data, the National Institutes of Health developed the Big Data to Knowledge or BD2K initiative. The concept of translating “big data to knowledge” is important to the social and behavioral sciences in several respects. First, a general shift to data-intensive science will exert an influence on all scientific disciplines, but particularly on the behavioral and social sciences given the wealth of behavior and related constructs captured by big data sources. Second, science is itself a social enterprise; by applying principles from the social sciences to the conduct of research, it should be possible to ameliorate some of the systemic problems that plague the scientific enterprise in the age of big data. We explore the feasibility of recalibrating the basic mechanisms of the scientific enterprise so that they are more transparent and cumulative; more integrative and cohesive; and more rapid, relevant, and responsive.

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Grid-Enabled Measures

Cited by 35 publications

References 35 publications

“It’s Valid and Reliable” Is Not Enough

“It’s Valid and Reliable” Is Not Enough

Behavioral Research in Cancer Prevention and Control

From Big Data to Knowledge in the Social Sciences

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