Systematic reviews: A glossary for public health
Bibliographic record
Abstract
Literature reviews are conducted for a range of purposes, from providing an overview or primer of a novel topic, to providing a comprehensive, precise, and accurate estimate of an effect estimate. There is much confusion over nomenclature related to literature reviews, with the term 'systematic review' often used to mean any review based on some form of explicit methodology. However, guidance and minimum standards exist for these kinds of robust reviews that are intended to support evidence-informed decision-making, and reviewers must carefully ensure their syntheses are conducted and reported to a high standard if this is their objective. The diversity of names given to reviews is reflected in the diversity of methods used for these evidence syntheses: the result is a general confusion about what is important to ensure a review is fit-for-purpose, and many reviews are labelled as 'systematic reviews' when they do not follow standardised or replicable approaches. Here, we provide a glossary or typology that aims to highlight the importance of the reviewers' objectives in choosing and naming their review method. We focus on reviews in public health and provide guidance on selecting an objective, methodological guidance to follow, justifying and reporting the methods chosen, and attempting to ensure consistent and clear nomenclature. We hope this will help review authors, editors, peer-reviewers, and readers understand, interpret, and critique a review depending on its intended use.
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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 | 0.029 | 0.139 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.038 | 0.049 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.073 | 0.043 |
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".