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Measurement of a model of implementation for health care: toward a testable theory
BACKGROUND: Greenhalgh et al. used a considerable evidence-base to develop a comprehensive model of implementation of innovations in healthcare organizations [1]. However, these authors did not fully operationalize their model, making it difficult to test formally. The present paper represents a fir...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3541168/ https://www.ncbi.nlm.nih.gov/pubmed/22759451 http://dx.doi.org/10.1186/1748-5908-7-59 |
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author | Cook, Joan M O’Donnell, Casey Dinnen, Stephanie Coyne, James C Ruzek, Josef I Schnurr, Paula P |
author_facet | Cook, Joan M O’Donnell, Casey Dinnen, Stephanie Coyne, James C Ruzek, Josef I Schnurr, Paula P |
author_sort | Cook, Joan M |
collection | PubMed |
description | BACKGROUND: Greenhalgh et al. used a considerable evidence-base to develop a comprehensive model of implementation of innovations in healthcare organizations [1]. However, these authors did not fully operationalize their model, making it difficult to test formally. The present paper represents a first step in operationalizing Greenhalgh et al.’s model by providing background, rationale, working definitions, and measurement of key constructs. METHODS: A systematic review of the literature was conducted for key words representing 53 separate sub-constructs from six of the model’s broad constructs. Using an iterative process, we reviewed existing measures and utilized or adapted items. Where no one measure was deemed appropriate, we developed other items to measure the constructs through consensus. RESULTS: The review and iterative process of team consensus identified three types of data that can been used to operationalize the constructs in the model: survey items, interview questions, and administrative data. Specific examples of each of these are reported. CONCLUSION: Despite limitations, the mixed-methods approach to measurement using the survey, interview measure, and administrative data can facilitate research on implementation by providing investigators with a measurement tool that captures most of the constructs identified by the Greenhalgh model. These measures are currently being used to collect data concerning the implementation of two evidence-based psychotherapies disseminated nationally within Department of Veterans Affairs. Testing of psychometric properties and subsequent refinement should enhance the utility of the measures. |
format | Online Article Text |
id | pubmed-3541168 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-35411682013-01-11 Measurement of a model of implementation for health care: toward a testable theory Cook, Joan M O’Donnell, Casey Dinnen, Stephanie Coyne, James C Ruzek, Josef I Schnurr, Paula P Implement Sci Research BACKGROUND: Greenhalgh et al. used a considerable evidence-base to develop a comprehensive model of implementation of innovations in healthcare organizations [1]. However, these authors did not fully operationalize their model, making it difficult to test formally. The present paper represents a first step in operationalizing Greenhalgh et al.’s model by providing background, rationale, working definitions, and measurement of key constructs. METHODS: A systematic review of the literature was conducted for key words representing 53 separate sub-constructs from six of the model’s broad constructs. Using an iterative process, we reviewed existing measures and utilized or adapted items. Where no one measure was deemed appropriate, we developed other items to measure the constructs through consensus. RESULTS: The review and iterative process of team consensus identified three types of data that can been used to operationalize the constructs in the model: survey items, interview questions, and administrative data. Specific examples of each of these are reported. CONCLUSION: Despite limitations, the mixed-methods approach to measurement using the survey, interview measure, and administrative data can facilitate research on implementation by providing investigators with a measurement tool that captures most of the constructs identified by the Greenhalgh model. These measures are currently being used to collect data concerning the implementation of two evidence-based psychotherapies disseminated nationally within Department of Veterans Affairs. Testing of psychometric properties and subsequent refinement should enhance the utility of the measures. BioMed Central 2012-07-03 /pmc/articles/PMC3541168/ /pubmed/22759451 http://dx.doi.org/10.1186/1748-5908-7-59 Text en Copyright ©2012 Cook et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Cook, Joan M O’Donnell, Casey Dinnen, Stephanie Coyne, James C Ruzek, Josef I Schnurr, Paula P Measurement of a model of implementation for health care: toward a testable theory |
title | Measurement of a model of implementation for health care: toward a testable theory |
title_full | Measurement of a model of implementation for health care: toward a testable theory |
title_fullStr | Measurement of a model of implementation for health care: toward a testable theory |
title_full_unstemmed | Measurement of a model of implementation for health care: toward a testable theory |
title_short | Measurement of a model of implementation for health care: toward a testable theory |
title_sort | measurement of a model of implementation for health care: toward a testable theory |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3541168/ https://www.ncbi.nlm.nih.gov/pubmed/22759451 http://dx.doi.org/10.1186/1748-5908-7-59 |
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