Economic evaluations of scaling up strategies of evidence-based health interventions: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: Scaling science aims to help roll out evidence-based research results on a wide scale to benefit more individuals. Yet, little is known on how to evaluate economic aspects of scaling up strategies of evidence-based health interventions. METHODS AND ANALYSIS: Using the Joanna Briggs Institute guidance on systematic reviews, we will conduct a systematic review of characteristics and methods applied in economic evaluations in scaling up strategies. To be eligible for inclusion, studies must include a scaling up strategy of an evidence-based health intervention delivered and received by any individual or organisation in any country and setting. They must report costs and cost-effectiveness outcomes. We will consider full or partial economic evaluations, modelling and methodological studies. We searched peer-reviewed publications in Medline, Web of Science, Embase, Cochrane Library Database, PEDE, EconLIT, INHATA from their inception onwards. We will search grey literature from international organisations, bilateral agencies, non-governmental organisations, consultancy firms websites and region-specific databases. Two independent reviewers will screen the records against the eligibility criteria and extract data using a pretested extraction form. We will extract data on study characteristics, scaling up strategies, economic evaluation methods and their components. We will appraise the methodological quality of included studies using the BMJ Checklist. We will narratively summarise the studies' descriptive characteristics, methodological strengths/weaknesses and the main drivers of cost-effectiveness outcomes. This study will help identify what are the trade-offs of scaling up evidence-based interventions to allocate resources efficiently. ETHICS AND DISSEMINATION: No ethics approval is required as no primary data will be collected. The results will be published in a peer-reviewed, international journal and presented at national and international conferences.
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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 | 0.169 | 0.187 |
| Meta-epidemiology (narrow) | 0.008 | 0.009 |
| Meta-epidemiology (broad) | 0.019 | 0.017 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.077 | 0.018 |
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".