MétaCan
Menu
Back to cohort
Record W4367053794 · doi:10.1111/1468-0009.12649

Evidence on Scaling in Health and Social Care: An Umbrella Review

2023· review· en· W4367053794 on OpenAlexafffund
Roberta de Carvalho Corôa, Amédé Gogovor, Ali Ben Charif, Asma Ben Hassine, Hervé Tchala Vignon Zomahoun, Robert K. D. McLean, Andrew Milat, Karine V. Plourde, Nathalie Rhéault, Luke Wolfenden, France Légaré

Bibliographic record

VenueMilbank Quarterly · 2023
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMcGill UniversityCentre intégré universitaire de santé et de services sociaux de la Capitale-NationaleUniversité LavalCentres Intégré Universitaires de Santé et de Services Sociaux
FundersNational Health and Medical Research CouncilMedical Research CouncilCanadian Institutes of Health ResearchUniversité Laval
KeywordsPsycINFOCINAHLMEDLINECochrane LibrarySystematic reviewHealth careMeta-analysisGlobal healthKnowledge translationMedicinePsychologyPolitical sciencePublic healthNursingComputer scienceKnowledge management

Abstract

fetched live from OpenAlex

Policy Points More rigorous methodologies and systematic approaches should be encouraged in the science of scaling. This will help researchers better determine the effectiveness of scaling, guide stakeholders in the scaling process, and ultimately increase the impacts of health innovations. The practice and the science of scaling need to expand worldwide to address complex health conditions such as noncommunicable and chronic diseases. Although most of the scaling experiences described in the literature are occurring in the Global South, most of the authors publishing on it are based in the Global North. As the science of scaling spreads across the world with the aim of reducing health inequities, it is also essential to address the power imbalance in how we do scaling research globally. CONTEXT: Scaling of effective innovations in health and social care is essential to increase their impact. We aimed to synthesize the evidence base on scaling and identify current knowledge gaps. METHODS: We conducted an umbrella review according to the Joanna Briggs Institute Reviewers' Manual. We included any type of review that 1) focused on scaling, 2) covered health or social care, and 3) presented a methods section. We searched MEDLINE (Ovid), Embase, PsycINFO (Ovid), CINAHL (EBSCO), Web of Science, The Cochrane Library, Sociological Abstracts (ProQuest), Academic Search Premier (EBSCO), and ProQuest Dissertations & Theses Global from their inception to August 6, 2020. We searched the gray literature using, e.g., Google and WHO-ExpandNet. We assessed methodological quality with AMSTAR2. Paired reviewers independently selected and extracted eligible reviews and assessed study quality. A narrative synthesis was performed. FINDINGS: Of 24,269 records, 137 unique reviews were included. The quality of the 58 systematic reviews was critically low (n = 42). The most frequent review type was systematic review (n = 58). Most reported on scaling in low- and middle-income countries (n = 59), whereas most first authors were from high-income countries (n = 114). Most reviews concerned infectious diseases (n = 36) or maternal-child health (n = 28). They mainly focused on interventions (n = 37), barriers and facilitators (n = 29), frameworks (n = 24), scalability (n = 24), and costs (n = 14). The WHO/ExpandNet scaling definition was the definition most frequently used (n = 26). Domains most reported as influencing scaling success were building scaling infrastructure (e.g., creating new service sites) and human resources (e.g., training community health care providers). CONCLUSIONS: The evidence base on scaling is evolving rapidly as reflected by publication trends, the range of focus areas, and diversity of scaling definitions. Our study highlights knowledge gaps around methodology and research infrastructures to facilitate equitable North-South research relationships. Common efforts are needed to ensure scaling expands the impacts of health and social innovations to broader populations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.131
metaresearch head score (Gemma)0.367
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.131
Threshold uncertainty score0.694

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1310.367
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0150.013
Bibliometrics0.0430.039
Science and technology studies0.0030.008
Scholarly communication0.0140.016
Open science0.0060.008
Research integrity0.0100.006
Insufficient payload (model declined to judge)0.0080.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.877
GPT teacher head0.746
Teacher spread0.131 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations31
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueMilbank QuarterlySame topicHealth Policy Implementation ScienceFrench-language works237,207