Evidence mapPaperPMID 41746914Full record

ArticlePloS one2026

Structural models for spreading and scaling digital health initiatives: A scoping review protocol.

Celia Laur, Karen Lee, Andrew Milat, Samuel Petrie, Zeenat Ladak, Vincci Lui, Alix Hall, Nicole Nathan, Priscilla Medeiros, Noah Ivers and 1 more

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Celia LaurOffice of Spread and Scale, Women's College Hospital Institute for Health System Solutions and Virtual Care, Toronto, Ontario, Canada.ORCID https://orcid.org/0000-0003-4555-1407
Karen LeePrevention Research Collaboration, Charles Perkins Centre, Faculty of Medicine and Health, Sydney School of Public Health, The University of Sydney, Camperdown, Australia.
Andrew MilatPrevention Research Collaboration, Charles Perkins Centre, Faculty of Medicine and Health, Sydney School of Public Health, The University of Sydney, Camperdown, Australia.
Samuel PetrieImplementation Science Team, Research, Innovation, and Discovery, Nova Scotia Health, Halifax, Canada.
Zeenat LadakOffice of Spread and Scale, Women's College Hospital Institute for Health System Solutions and Virtual Care, Toronto, Ontario, Canada.
Vincci LuiGerstein Science Information Centre, University of Toronto, Toronto, Ontario, Canada.
Alix HallSchool of Medicine and Public Health, Faculty of Health and Medicine, University of Newcastle, Newcastle, New South Wales, Australia.
Nicole NathanSchool of Medicine and Public Health, Faculty of Health and Medicine, University of Newcastle, Newcastle, New South Wales, Australia.
Priscilla MedeirosEdwin S.H. Leong Centre for Healthy Children, University of Toronto, Toronto, Ontario, Canada.ORCID https://orcid.org/0000-0003-1050-6067
Noah IversOffice of Spread and Scale, Women's College Hospital Institute for Health System Solutions and Virtual Care, Toronto, Ontario, Canada.ORCID https://orcid.org/0000-0003-2500-2435
Onil BhattacharyyaOffice of Spread and Scale, Women's College Hospital Institute for Health System Solutions and Virtual Care, Toronto, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHealthcare initiatives have a larger impact if effective initiatives are spread (brought from one site to the next) or scaled (infrastructure developed to underpin and support widespread implementation), while sustaining initial benefits. Unfortunately, many initiatives, including digital health initiatives, remain confined to the pilot stage. Of those initiatives that do progress, little is known about how to plan for the equitable spread and scale of effective initiatives. There are many structural "models" of spread and scale, defined here as conceptual representations of how initiatives are organised and delivered across multiple settings (i.e., hub-and-spoke model), yet little is known about these models.

objectivesPrimary Objective: To identify and describe structural models for spreading and scaling digital health initiatives. Secondary Objectives: 1. To describe the associated factors, strengths, limitations, and necessary preconditions associated with each model. 2. To describe the barriers and facilitators experienced when applying each model. 3. To explore whether and how each model prioritized equitable delivery of care. 4. To determine which pre-established types of scale (horizontal, vertical, diversification, and spontaneous) are associated with each model.

methodsA scoping review will be conducted following Joanna Briggs Institute (JBI) methodology and reported in accordance with PRISMA-ScR guidelines. The search strategy includes peer-reviewed databases for health and business, alongside grey literature sources. Eligibility criteria follow the Population-Concept-Context framework, focusing on digital health initiatives delivered in healthcare settings.

resultsThe review will produce a comprehensive overview of structural models for spreading and scaling digital health initiatives, including model names, descriptions, strengths, limitations, preconditions, associated barriers and facilitators of applying each model, relationships between models and established types of scale, and equity considerations.

conclusionsThis novel review aims to inform practical planning of how to bring digital health initiatives to new settings and populations, to support more equitable access to these initiatives.

Indexed as

Delivery of Health CareDigital HealthHumansScoping Reviews as Topic

Identifiers

PMID41746914
PMCPMC12944785

What Socratic holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.