State‐of‐the‐Evidence Reviews: Advantages and Challenges of Including Grey Literature
Bibliographic record
Abstract
BACKGROUND: Increasingly, health policy decision-makers and professionals are turning to research-based evidence to support decisions about policy and practice. Systematic reviews are useful for gathering, summarizing, and synthesizing published and unpublished research about clearly defined interventions. State-of-the-evidence reviews are broader than traditional systematic reviews and may include not only published and unpublished research, but also published and unpublished non-research literature. Decisions about whether to include this "grey literature" in a review are challenging and lead to many questions about whether the advantages outweigh the challenges. AIMS: The primary purpose of this article is to describe what constitutes grey literature, and methods to locate it and assess its quality. The secondary purpose is to discuss the core issues to consider when making decisions to include grey literature in a state-of-the-evidence review. METHODS: A recent state-of-the-evidence review is used as an exemplar to present advantages and challenges related to including grey literature in a review. RESULTS: Despite the challenges, in the exemplar, inclusion of grey literature was useful to validate the results of a research-based literature search. CONCLUSION: Decisions about whether to include grey literature in a state-of-the-evidence review are complex. A checklist to assist in decision-making was created as a tool to assist the researcher in determining whether it is advantageous to include grey literature in a review.
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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 | 0.597 | 0.785 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.036 | 0.025 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.039 | 0.044 |
| Open science | 0.008 | 0.023 |
| Research integrity | 0.016 | 0.014 |
| 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; 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".