Evidence on scaling in health and social care: protocol for a living umbrella review
Bibliographic record
Abstract
BACKGROUND: There is a growing interest in scaling effective health innovations to promote equitable access to high-quality health services worldwide. However, multiple challenges persist in scaling innovations. In this study, we aim to summarize the scaling evidence in the health and social care literature and identify current knowledge gaps. METHODS: We will conduct a living umbrella review according to the Joanna Briggs Institute Reviewers' Manual. We will consider all knowledge syntheses addressing scaling in health or social care (e.g., any setting, any clinical area) and conducted in a systematic way. We will search the following electronic databases: MEDLINE (Ovid), Embase, PsychINFO (Ovid), CINAHL (EBSCO), Web of Science, The Cochrane Library, Sociological Abstract (Proquest), Academic Search Premier (EBSCO), and Proquest Dissertations & Theses Global, from inception. Furthermore, we will conduct searches of the grey literature. No restriction regarding date or language will be applied. Each phase of the review will be processed by two independent reviewers. We will develop a data extraction form on Covidence. We will assess the methodological quality of the included reviews using AMSTAR2 and the risk of bias using ROBIS. Results will be presented in tabular form and accompanied by a narrative synthesis covering the traditional themes of scaling science that emerge from the analysis, such as coverage, range, and sustainability, as well as themes less covered in the literature, including reporting guidance, models, tools, barriers, and/or facilitators to scaling innovations, evidence regarding application in high-income or low-income countries, and end-user engagement. We will disseminate the findings via publications and through relevant networks. DISCUSSION: The findings of the umbrella review will facilitate access to scaling evidence in the literature and help strengthen the science of scaling for researchers, policy makers, and program managers. Finally, this work will highlight important knowledge gaps and help prioritize future research questions. SYSTEMATIC REVIEW REGISTRATION: This protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) on November 11, 2020 (registration number: CRD42020183774 ).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".