What is the most appropriate knowledge synthesis method to conduct a review? Protocol for a scoping review
Bibliographic record
Abstract
BACKGROUND: A knowledge synthesis attempts to summarize all pertinent studies on a specific question, can improve the understanding of inconsistencies in diverse evidence, and can identify gaps in research evidence to define future research agendas. Knowledge synthesis activities in healthcare have largely focused on systematic reviews of interventions. However, a wider range of synthesis methods has emerged in the last decade addressing different types of questions (e.g., realist synthesis to explore mediating mechanisms and moderators of interventions). Many different knowledge synthesis methods exist in the literature across multiple disciplines, but locating these, particularly for qualitative research, present challenges. There is a need for a comprehensive manual for synthesis methods (quantitative/qualitative or mixed), outlining how these methods are related, and how to match the most appropriate knowledge synthesis method to answer a research question. The objectives of this scoping review are to: 1) conduct a systematic search of the literature for knowledge synthesis methods across multi-disciplinary fields; 2) compare and contrast the different knowledge synthesis methods; and, 3) map out the specific steps to conducting the knowledge syntheses to inform the development of a knowledge synthesis methods manual/tool. METHODS: We will search relevant electronic databases (e.g., MEDLINE, CINAHL), grey literature, and discipline-based listservs. The scoping review will consider all study designs including qualitative and quantitative methodologies (excluding economic analysis or clinical practice guideline development), and identify knowledge synthesis methods across the disciplines of health, education, sociology, and philosophy. Two reviewers will pilot-test the screening criteria and data abstraction forms, and will independently screen the literature and abstract the data. A three-step synthesis process will be used to map the literature to our objectives. DISCUSSION: This project represents the first attempt to broadly and systematically identify, define and classify knowledge synthesis methods (i.e., less traditional knowledge synthesis methods). We anticipate that our results will lead to an accepted taxonomy for less traditional knowledge synthesis methods, and to the development and implementation of a methods manual for these reviews which will be relevant to a wide range of knowledge users, including researchers, funders, and journal editors.
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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 | 0.270 | 0.425 |
| Meta-epidemiology (narrow) | 0.008 | 0.007 |
| Meta-epidemiology (broad) | 0.017 | 0.022 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.077 | 0.026 |
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".