Advancing scaling science in health and social care: a scoping review and appraisal of scaling frameworks
Bibliographic record
Abstract
BACKGROUND: Scaling is typically discussed as a way to amplify or expand a health innovation. However, there is limited knowledge about the specific techniques that can enhance access to or improve the quality of innovations, aiming to increase their positive impacts for the public good. We sought to identify, compare, and contrast scaling frameworks to advance the science and practice of scaling. METHODS: Using a scoping review we asked: 1) What are the attributes of scaling frameworks for innovations that support health outcomes? and 2) What are the similarities and differences of these attributes? Inclusion criteria were 1) primary studies or review articles, 2) a primary focus on scaling innovations for health and social care, 3) articles that developed a framework, and 4) articles were concerned with a health outcome. Starting from an umbrella review, we identified relevant studies and extracted data about the characteristics of the articles, attributes of framework development, attributes of framework components, transferability, and the framework's underlying ethical lens. Grey literature was included through expert consultation. Data were summarized using frequencies and qualitative description. RESULTS: From 94 potentially eligible articles, we identified 9 unique frameworks and included 4 additional frameworks from the grey literature, resulting in a total of 13 frameworks. Seven frameworks include a definition of scaling, and eight are designed for public health settings. Five of the frameworks were developed for the US/Canada/UK and Australia. Six of the lead authors' primary institutional affiliation are from North America. Framework developers involved diverse stakeholders in a number of ways to develop their framework. Eight frameworks were developed, but not yet tested or applied, while the remaining frameworks were in the process of being applied or had already been applied to cases. All frameworks use a consequentialist-utilitarian ethical lens. Lastly, a comparison between frameworks found in the grey or published literature show important differences. CONCLUSION: Much may be learned through further support for, and development of, scaling frameworks by primary authors affiliated with the Global South. Important aspects of framework development were identified, especially understanding the nuances of diverse stakeholder involvement in development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.241 | 0.474 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.079 | 0.063 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".