Designing, planning, and conducting systematic reviews and other knowledge syntheses: Six key practical recommendations to improve feasibility and efficiency
Bibliographic record
Abstract
BACKGROUND: Knowledge syntheses, such as systematic reviews, scoping reviews, and realist reviews, are crucial tools to guide nursing practice, policy, and research. However, conducting high-quality knowledge syntheses is a complex and time-consuming endeavor. It is imperative for nursing students, clinicians, and researchers to be aware of key practical recommendations regarding the conduct of knowledge syntheses to improve the feasibility and efficiency of such projects. AIM: The aim of this paper was to discuss key practical recommendations for designing, planning, and conducting knowledge syntheses relevant to nursing policy, practice, and research. METHODS: The recommendations discussed are based on best-practice guidance about knowledge synthesis methodology proposed by The Campbell Collaboration (Campbell systematic reviews: Policies and guidelines, 2020), Cochrane (Cochrane training, 2019), and the Joanna Briggs Institute (The Joanna Briggs Institute reviewers' manual, 2020) and on strategies used by the authors to improve the feasibility and efficiency of knowledge syntheses. RESULTS: This paper highlights six key practical recommendations that nursing students, clinicians, and researchers should take into account when deciding to embark on a knowledge synthesis project: (1) determining if (and why) knowledge synthesis should be conducted; (2) selecting the appropriate type of knowledge synthesis, as well as the associated methodological guidance and reporting standards; (3) developing a search strategy that balances sensitivity and specificity; (4) writing a protocol and obtaining feedback; (5) determining the resources required to conduct the different stages of the knowledge synthesis; and (6) keeping an audit trail. Fifteen common types of knowledge synthesis are presented with their definitions, relevant methodological guidance, and reporting standards. LINKING EVIDENCE TO ACTION: The recommendations discussed, used in conjunction with appropriate methodological guidelines, may help ensure the success of a knowledge synthesis project by providing best-practice and experience-based guidance to newcomers in the field.
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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 | 0.840 | 0.937 |
| Meta-epidemiology (narrow) | 0.009 | 0.013 |
| Meta-epidemiology (broad) | 0.017 | 0.020 |
| Bibliometrics | 0.030 | 0.031 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.035 | 0.049 |
| Open science | 0.013 | 0.022 |
| Research integrity | 0.035 | 0.029 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".