“How-to”: scoping review?
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Scoping reviews are a type of evidence synthesis that aims to identify and map the breadth of evidence available on a particular topic, field, concept, or issue, within or across a defined context or contexts. Scoping reviews can contribute to clinical practice guideline development, policy making, reduce research waste by eliminating duplication of research effort, and be a precursor to a systematic review or inform further primary research. This article aims to provide a brief introduction of how to conduct and report scoping reviews. STUDY DESIGN AND SETTING: We will discuss the role and value of scoping reviews within the evidence synthesis ecosystem, the differences and similarities between these reviews and other types of evidence syntheses such as systematic reviews, mapping reviews, evidence and gap maps, and overviews, and how to overcome common challenges often associated in the conduct, reporting, and dissemination of scoping reviews. RESULTS: Scoping reviews have a role in the evidence ecosystem; however, we need to acknowledge their challenges. CONCLUSION: Scoping reviews are a popular form of evidence synthesis, and further research is needed to provide clarity of current methodological challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.324 | 0.684 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.021 | 0.030 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.031 | 0.036 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.018 | 0.016 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".