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How to optimise public health interventions: a scoping review of guidance from optimisation process frameworks

BACKGROUND: Optimisation processes have the potential to rapidly improve the impact of health interventions. Optimisation can be defined as a deliberate, iterative and data-driven process to improve a health intervention and/or its implementation to meet stakeholder-defined public health impacts wit...

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Autores principales: McCrabb, Sam, Mooney, Kaitlin, Elton, Benjamin, Grady, Alice, Yoong, Sze Lin, Wolfenden, Luke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7709329/
https://www.ncbi.nlm.nih.gov/pubmed/33267844
http://dx.doi.org/10.1186/s12889-020-09950-5
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author McCrabb, Sam
Mooney, Kaitlin
Elton, Benjamin
Grady, Alice
Yoong, Sze Lin
Wolfenden, Luke
author_facet McCrabb, Sam
Mooney, Kaitlin
Elton, Benjamin
Grady, Alice
Yoong, Sze Lin
Wolfenden, Luke
author_sort McCrabb, Sam
collection PubMed
description BACKGROUND: Optimisation processes have the potential to rapidly improve the impact of health interventions. Optimisation can be defined as a deliberate, iterative and data-driven process to improve a health intervention and/or its implementation to meet stakeholder-defined public health impacts within resource constraints. This study aimed to identify frameworks used to optimise the impact of health interventions and/or their implementation, and characterise the key concepts, steps or processes of identified frameworks. METHODS: A scoping review of MEDLINE, CINAL, PsycINFO, and ProQuest Nursing & Allied Health Source databases was undertaken. Two reviewers independently coded the key concepts, steps or processes involved in each frameworks, and identified if it was a framework aimed to optimise interventions or their implementation. Two review authors then identified the common steps across included frameworks. RESULTS: Twenty optimisation frameworks were identified. Eight frameworks were for optimising interventions, 11 for optimising implementation and one covered both intervention and implementation optimisation. The mean number of steps within the frameworks was six (range 3–9). Almost half (n = 8) could be classified as both linear and cyclic frameworks, indicating that some steps may occur multiple times in a single framework. Two meta-frameworks are proposed, one for intervention optimisation and one for implementation strategy optimisation. Steps for intervention optimisation are: Problem identification; Preparation; Theoretical/Literature base; Pilot/Feasibility testing; Optimisation; Evaluation; and Long-term implementation. Steps for implementation strategy optimisation are: Problem identification; Collaborate; Plan/design; Pilot; Do/change; Study/evaluate/check; Act; Sustain/endure; and Disseminate/extend. CONCLUSIONS: This review provides a useful summary of the common steps followed to optimise a public health intervention or its implementation according to established frameworks. Further opportunities to study and/or validate such frameworks and their impact on improving outcomes exist. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-020-09950-5.
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spelling pubmed-77093292020-12-02 How to optimise public health interventions: a scoping review of guidance from optimisation process frameworks McCrabb, Sam Mooney, Kaitlin Elton, Benjamin Grady, Alice Yoong, Sze Lin Wolfenden, Luke BMC Public Health Research Article BACKGROUND: Optimisation processes have the potential to rapidly improve the impact of health interventions. Optimisation can be defined as a deliberate, iterative and data-driven process to improve a health intervention and/or its implementation to meet stakeholder-defined public health impacts within resource constraints. This study aimed to identify frameworks used to optimise the impact of health interventions and/or their implementation, and characterise the key concepts, steps or processes of identified frameworks. METHODS: A scoping review of MEDLINE, CINAL, PsycINFO, and ProQuest Nursing & Allied Health Source databases was undertaken. Two reviewers independently coded the key concepts, steps or processes involved in each frameworks, and identified if it was a framework aimed to optimise interventions or their implementation. Two review authors then identified the common steps across included frameworks. RESULTS: Twenty optimisation frameworks were identified. Eight frameworks were for optimising interventions, 11 for optimising implementation and one covered both intervention and implementation optimisation. The mean number of steps within the frameworks was six (range 3–9). Almost half (n = 8) could be classified as both linear and cyclic frameworks, indicating that some steps may occur multiple times in a single framework. Two meta-frameworks are proposed, one for intervention optimisation and one for implementation strategy optimisation. Steps for intervention optimisation are: Problem identification; Preparation; Theoretical/Literature base; Pilot/Feasibility testing; Optimisation; Evaluation; and Long-term implementation. Steps for implementation strategy optimisation are: Problem identification; Collaborate; Plan/design; Pilot; Do/change; Study/evaluate/check; Act; Sustain/endure; and Disseminate/extend. CONCLUSIONS: This review provides a useful summary of the common steps followed to optimise a public health intervention or its implementation according to established frameworks. Further opportunities to study and/or validate such frameworks and their impact on improving outcomes exist. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-020-09950-5. BioMed Central 2020-12-02 /pmc/articles/PMC7709329/ /pubmed/33267844 http://dx.doi.org/10.1186/s12889-020-09950-5 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
McCrabb, Sam
Mooney, Kaitlin
Elton, Benjamin
Grady, Alice
Yoong, Sze Lin
Wolfenden, Luke
How to optimise public health interventions: a scoping review of guidance from optimisation process frameworks
title How to optimise public health interventions: a scoping review of guidance from optimisation process frameworks
title_full How to optimise public health interventions: a scoping review of guidance from optimisation process frameworks
title_fullStr How to optimise public health interventions: a scoping review of guidance from optimisation process frameworks
title_full_unstemmed How to optimise public health interventions: a scoping review of guidance from optimisation process frameworks
title_short How to optimise public health interventions: a scoping review of guidance from optimisation process frameworks
title_sort how to optimise public health interventions: a scoping review of guidance from optimisation process frameworks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7709329/
https://www.ncbi.nlm.nih.gov/pubmed/33267844
http://dx.doi.org/10.1186/s12889-020-09950-5
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