Ali Ben Charif1, Kasra Hassani1, Sabrina T Wong1, Hervé Tchala Vignon Zomahoun1, Martin Fortin1, Adriana Freitas1, Alan Katz1, Claire E Kendall1, Clare Liddy1, Kathryn Nicholson1, Bojana Petrovic1, Jenny Ploeg1, France Légaré2. 1. Centre de recherche sur les soins et les services de première ligne (Ben Charif, Zomahoun, Freitas, Légaré); Health and Social Services Systems, Knowledge Translation and Implementation component (Ben Charif, Zomahoun, Légaré), Quebec Strategy for Patient-Oriented Research (SPOR) Support for People and Patient-Oriented Research and Trials (SUPPORT) Unit; Tier 1 Canada Research Chair in Shared Decision Making and Knowledge Translation (Ben Charif, Freitas, Légaré); Department of Family Medicine and Emergency Medicine (Ben Charif, Légaré), Université Laval, Québec, Que.; School of Nursing (Hassani, Wong) and Centre for Health Services and Policy Research (Hassani, Wong), University of British Columbia, Vancouver, BC; Department of Family Medicine and Emergency Medicine (Fortin), Université de Sherbrooke, Sherbrooke, Que.; Diabetes Action Canada (Freitas, Légaré), Université Laval, Québec, Que.; Departments of Community Health Sciences (Katz) and Family Medicine (Katz), Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Man.; Élisabeth Bruyère Research Institute (Kendall, Liddy), C.T. Lamont Primary Health Care Research Group; Department of Family Medicine (Kendall, Liddy) and Ottawa Hospital Research Institute (Kendall), University of Ottawa, Ottawa, Ont.; Li Ka Shing Knowledge Institute (Kendall), St. Michael's Hospital, Toronto, Ont.; Department of Epidemiology and Biostatistics (Nicholson), Western University, London, Ont.; Department of Family and Community Medicine (Petrovic) and Dalla Lana School of Public Health (Petrovic), University of Toronto, Toronto, Ont.; School of Nursing (Ploeg), Faculty of Health Sciences, and Diabetes Action Canada (Ploeg), McMaster University, Hamilton, Ont. 2. Centre de recherche sur les soins et les services de première ligne (Ben Charif, Zomahoun, Freitas, Légaré); Health and Social Services Systems, Knowledge Translation and Implementation component (Ben Charif, Zomahoun, Légaré), Quebec Strategy for Patient-Oriented Research (SPOR) Support for People and Patient-Oriented Research and Trials (SUPPORT) Unit; Tier 1 Canada Research Chair in Shared Decision Making and Knowledge Translation (Ben Charif, Freitas, Légaré); Department of Family Medicine and Emergency Medicine (Ben Charif, Légaré), Université Laval, Québec, Que.; School of Nursing (Hassani, Wong) and Centre for Health Services and Policy Research (Hassani, Wong), University of British Columbia, Vancouver, BC; Department of Family Medicine and Emergency Medicine (Fortin), Université de Sherbrooke, Sherbrooke, Que.; Diabetes Action Canada (Freitas, Légaré), Université Laval, Québec, Que.; Departments of Community Health Sciences (Katz) and Family Medicine (Katz), Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Man.; Élisabeth Bruyère Research Institute (Kendall, Liddy), C.T. Lamont Primary Health Care Research Group; Department of Family Medicine (Kendall, Liddy) and Ottawa Hospital Research Institute (Kendall), University of Ottawa, Ottawa, Ont.; Li Ka Shing Knowledge Institute (Kendall), St. Michael's Hospital, Toronto, Ont.; Department of Epidemiology and Biostatistics (Nicholson), Western University, London, Ont.; Department of Family and Community Medicine (Petrovic) and Dalla Lana School of Public Health (Petrovic), University of Toronto, Toronto, Ont.; School of Nursing (Ploeg), Faculty of Health Sciences, and Diabetes Action Canada (Ploeg), McMaster University, Hamilton, Ont. france.legare@mfa.ulaval.ca.
Abstract
BACKGROUND: In 2013, the Canadian Institutes of Health Research funded 12 community-based primary health care research teams to develop evidence-based innovations. We aimed to explore the scalability of these innovations. METHODS: In this cross-sectional study, we invited the 12 teams to rate their evidence-based innovations for scalability. Based on a systematic review, we developed a self-administered questionnaire with 16 scalability assessment criteria grouped into 5 dimensions (theory, impact, coverage, setting and cost). Teams completed a questionnaire for each of their innovations. We analyzed the data using simple frequency counts and hierarchical cluster analysis. We calculated the mean number and standard deviation (SD) of innovations that met criteria within each dimension that included more than 1 criterion. The analysis unit was the innovation. RESULTS: The 11 responding teams evaluated 33 evidence-based innovations (median 3, range 1-8 per team). The innovations focused on access to care and chronic disease prevention and management, and varied from health interventions to methodological innovations. Most of the innovations were health interventions (n = 21), followed by analytical methods (n = 4), conceptual frameworks (n = 4), measures (n = 3) and strategies to build research capacity (n = 1). Most (29) met criteria in the theory dimension, followed by impact (mean 22.3 [SD 5.6] innovations per dimension), setting (mean 21.7 [SD 8.5]), cost (mean 17.5 [SD 2.1]) and coverage (mean 14.0 [SD 4.1]). On average, the innovations met 10 of the 16 criteria. Adoption was the least assessed criterion (n = 9). Most (20) of the innovations were highly ranked for scalability. INTERPRETATION: Scalability varied among innovations, which suggests that readiness for scale up was suboptimal for some innovations. Coverage remained largely unaddressed; further investigation of this critical dimension is necessary. Copyright 2018, Joule Inc. or its licensors.
BACKGROUND: In 2013, the Canadian Institutes of Health Research funded 12 community-based primary health care research teams to develop evidence-based innovations. We aimed to explore the scalability of these innovations. METHODS: In this cross-sectional study, we invited the 12 teams to rate their evidence-based innovations for scalability. Based on a systematic review, we developed a self-administered questionnaire with 16 scalability assessment criteria grouped into 5 dimensions (theory, impact, coverage, setting and cost). Teams completed a questionnaire for each of their innovations. We analyzed the data using simple frequency counts and hierarchical cluster analysis. We calculated the mean number and standard deviation (SD) of innovations that met criteria within each dimension that included more than 1 criterion. The analysis unit was the innovation. RESULTS: The 11 responding teams evaluated 33 evidence-based innovations (median 3, range 1-8 per team). The innovations focused on access to care and chronic disease prevention and management, and varied from health interventions to methodological innovations. Most of the innovations were health interventions (n = 21), followed by analytical methods (n = 4), conceptual frameworks (n = 4), measures (n = 3) and strategies to build research capacity (n = 1). Most (29) met criteria in the theory dimension, followed by impact (mean 22.3 [SD 5.6] innovations per dimension), setting (mean 21.7 [SD 8.5]), cost (mean 17.5 [SD 2.1]) and coverage (mean 14.0 [SD 4.1]). On average, the innovations met 10 of the 16 criteria. Adoption was the least assessed criterion (n = 9). Most (20) of the innovations were highly ranked for scalability. INTERPRETATION: Scalability varied among innovations, which suggests that readiness for scale up was suboptimal for some innovations. Coverage remained largely unaddressed; further investigation of this critical dimension is necessary. Copyright 2018, Joule Inc. or its licensors.
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