| Title |
Assessing multilevel determinants of adoption and implementation of genomic medicine: an organizational mixed-methods approach
|
|---|---|
| Published in |
Genetics in Medicine, March 2015
|
| DOI | 10.1038/gim.2015.7 |
| Pubmed ID | |
| Authors |
Sabine M. Oishi, Nell Marshall, Alison B. Hamilton, Elizabeth M. Yano, Barbara Lerner, Maren T. Scheuner |
| Abstract |
Purpose:Adoption and implementation of evidence-based genetic and genomic medicine have been slow. We describe a methodology for identifying the influence of organizational factors on adoption and implementation of these services in health-care organizations.Methods:We illustrate a three-component, mixed-methods health services research approach, including expert panels, qualitative interviews with key informants, and quantitative surveys completed by key informants.Results:This research approach yielded a baseline assessment of existing genetic health-care models in the Veterans Health Administration and identified organizational barriers to and facilitators of adoption. In aggregate, the panel and key informant strategies created a communication network of relevant organizational stakeholders and a detailed foundation of organizational knowledge from which to design tools and models for implementation-level genetic/genomic translation.Conclusion:Expert panel and key informant strategies can be used to create a backdrop of stakeholder involvement and baseline organizational knowledge within which to plan translation research and to inform strategic planning and policies for adoption and implementation of genetic services in health-care organizations.Genet Med advance online publication 05 March 2015Genetics in Medicine (2015); doi:10.1038/gim.2015.7. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Science communicators (journalists, bloggers, editors) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 45 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 16% |
| Researcher | 7 | 16% |
| Student > Master | 4 | 9% |
| Student > Doctoral Student | 3 | 7% |
| Other | 2 | 4% |
| Other | 6 | 13% |
| Unknown | 16 | 36% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 8 | 18% |
| Nursing and Health Professions | 5 | 11% |
| Social Sciences | 4 | 9% |
| Biochemistry, Genetics and Molecular Biology | 3 | 7% |
| Engineering | 2 | 4% |
| Other | 6 | 13% |
| Unknown | 17 | 38% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 March 2015.
All research outputs
#16,722,913
of 25,374,917 outputs
Outputs from Genetics in Medicine
#2,560
of 2,943 outputs
Outputs of similar age
#156,157
of 272,868 outputs
Outputs of similar age from Genetics in Medicine
#43
of 49 outputs
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