The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Narrative review of models and success factors for scaling up public health interventions
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BACKGROUND: To maximise the impact of public health research, research interventions found to be effective in improving health need to be scaled up and delivered on a population-wide basis. Theoretical frameworks and approaches are useful for describing and understanding how effective interventions are scaled up from small trials into broader policy and practice and can be used as a tool to facilitate effective scale-up. The purpose of this literature review was to synthesise evidence on scaling up public health interventions into population-wide policy and practice, with a focus on the defining and describing frameworks, processes and methods of scaling up public health initiatives. METHODS: The review involved keyword searches of electronic databases including MEDLINE, CINAHL, PsycINFO, EBM Reviews and Google Scholar between August and December 2013. Keywords included 'scaling up' and 'scalability', while the search terms 'intervention research', 'translational research', 'research dissemination', 'health promotion' and 'public health' were used to focus the search on public health approaches. Studies included in the review were published in English from January 1990 to December 2013 and described processes, theories or frameworks associated with scaling up public health and health promotion interventions. RESULTS: There is a growing body of literature describing frameworks for scaling health interventions, with the review identifying eight frameworks, the majority of which have an explicit focus on scaling up health action in low and middle income country contexts. Key success factors for scaling up included the importance of establishing monitoring and evaluation systems, costing and economic modelling of intervention approaches, active engagement of a range of implementers and the target community, tailoring the scaled-up approach to the local context, the use of participatory approaches, the systematic use of evidence, infrastructure to support implementation, strong leadership and champions, political will, well defined scale-up strategy and strong advocacy. CONCLUSIONS: Effective scaling up requires the systematic use of evidence, and it is essential that data from implementation monitoring is linked to decision making throughout the scaling up process. Conceptual frameworks can assist both policy makers and researchers to determine the type of research that is most useful at different stages of scaling up processes.
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“Examples of scale-up strategies include train-the-trainer initiatives, infrastructure development (supply chains, data systems), quality improvement collaboratives, benchmarking, policy advocacy, and recognition systems [42, 43].”
From Beyond “implementation strategies”: classifying the full range of strategies used in implementation science and practice (2017) · cites this paper as [42]
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“Targets level of individual, inner setting, and outer setting”
From Beyond “implementation strategies”: classifying the full range of strategies used in implementation science and practice (2017) · cites this paper as [42]
In the citing paper
“Motivation and capacity to integrate a specific EBI into practice”
From Beyond “implementation strategies”: classifying the full range of strategies used in implementation science and practice (2017) · cites this paper as [42]
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“Motivation and capacity to implement, and actual implementation of an EBI across multiple settings”
From Beyond “implementation strategies”: classifying the full range of strategies used in implementation science and practice (2017) · cites this paper as [42]
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Reis, R. S., Salvo, D., Ogilvie, D., Lambert, E. V., Goenka, S., & Brownson, R. C. (2016) · The Lancet · doi:10.1016/s0140-6736(16)30728-0 · Cited by 719
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Koorts, H., Eakin, E., Estabrooks, P. A., Timperio, A. F., Salmon, J., & Bauman, A. E. (2018) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-018-0678-0 · Cited by 337
Self-citationIowa Model of Evidence‐Based Practice: Revisions and Validation
Collaborative, I. M., Buckwalter, K. C., Cullen, L., Hanrahan, K., Kleiber, C., McCarthy, A. M., Rakel, B. A., Steelman, V. M., Tripp‐Reimer, T., Tucker, S., et al. (2017) · Worldviews on Evidence-Based Nursing · doi:10.1111/wvn.12223 · Cited by 324
Mishra, M., Sudarsan, D., Santos, C. A. G., Mishra, S. K., Kamal, A. H. M., Goswami, S., Kalumba, A. M., Biswal, R., da Silva, R. M., dos Santos, C. A. C., et al. (2023) · Environment Development and Sustainability · doi:10.1007/s10668-023-03225-w · Cited by 416
Metadata
- Authors
- Milat, A. J., Bauman, A., & Redman, S.
- Affiliations
- The University of Sydney (AU)
- Sax Institute (AU)
- Corresponding author
- Milat, Andrew J.
- Venue
- Implementation Science
- Published
- 2015-08-12
- Volume
- 10
- Issue
- 1
- Pages
- 113-113
- ISSN
- 1748-5908 (ISSN-L)
- PMC
- PMC4533941
- OpenAlex
- W2160180972
- Cited by
- 366
- References
- 44
- Citations by year
- 2026: 26 · 2025: 32 · 2024: 28 · 2023: 48 · 2022: 45 · 2021: 58 · 2020: 39 · 2019: 36 · 2018: 29 · 2017: 14 · 2016: 10 · 2015: 1
- FWCI
- 3.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Implementation Science (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › General Health Professions › Health Policy Implementation Science
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Public Health Policies and Education
- Social Sciences › Public Administration › Evaluation and Performance Assessment
- Keywords
- scale-up, public health interventions, success factors, health promotion, translational research, research dissemination, implementation monitoring and evaluation, participatory approach, political will, advocacy, leadership, low- and middle-income countries, evidence-based decision making
- MeSH terms
- Humans, Public Health Administration, Program Evaluation, Program Development, Models, Organizational, Capacity Building
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