Evidence on Scaling in Health and Social Care: An Umbrella Review
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,131 | 0,367 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,003 |
| Méta-épidémiologie (sens large) | 0,015 | 0,013 |
| Bibliométrie | 0,043 | 0,039 |
| Études des sciences et des technologies | 0,003 | 0,008 |
| Communication savante | 0,014 | 0,016 |
| Science ouverte | 0,006 | 0,008 |
| Intégrité de la recherche | 0,010 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».