Systems for assessing the certainty or confidence of evidence in health care: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: This scoping review aims to identify existing systems, frameworks, or approaches for assessing certainty or confidence in quantitative, qualitative, and mixed methods evidence, providing a foundation for developing a unified framework tailored to mixed methods reviews. INTRODUCTION: Assessing the certainty or confidence in evidence is essential for developing health care recommendations, yet current frameworks are often limited to either quantitative or qualitative paradigms. With the rise of mixed methods research, which integrates quantitative and qualitative evidence to address complex health care questions, there is a growing need for systems capable of evaluating certainty across these diverse evidence types. ELIGIBILITY CRITERIA: This scoping review will include systems, frameworks, or approaches explicitly developed to assess the certainty or confidence in evidence from quantitative, qualitative, or mixed methods studies. Eligible papers must describe the methodology, criteria, or principles of these systems or discuss their development, validation, or theoretical foundations. Systems focused solely on critical appraisal or quality assessment of individual studies will be excluded unless they integrate these assessments into a broader framework for assessing certainty in a body of evidence. METHODS: This review will be conducted in accordance with the JBI methodology for scoping reviews and reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) guidelines. A comprehensive 3-step search strategy will identify published, unpublished, and gray literature from databases, organizational websites, and reference lists. Data will be extracted using a piloted extraction table and presented in tables, figures, and a narrative summary to map existing systems, frameworks, or approaches for assessing certainty or confidence in evidence. REVIEW REGISTRATION: OSF https://osf.io/36n78/.
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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 | 0.290 | 0.311 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.010 | 0.016 |
| Bibliometrics | 0.030 | 0.027 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.083 | 0.029 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".