MAPPING KNOWLEDGE IN SCOPING REVIEWS: A GUIDE FOR NURSING RESEARCHERS AND ACADEMICS
Bibliographic record
Abstract
ABSTRACT Objective: to provide nursing researchers with a comprehensive framework for conducting high-quality scoping reviews and enhance the process of evidence-based care. Method: Drawing upon existing literature, this paper synthesizes insights from existing guidance with a focus in adapting the information for the nursing community. Furthermore, it introduces a proposed 10-step framework for conducting scoping reviews, specific for nursing researchers, amalgamating guidance from reputable healthcare research organizations such as the Joanna Briggs Institute (JBI) and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) extension for Scoping Reviews. Results: In an era marked by the information deluge, nursing researchers hold a pivotal role in conducting scoping reviews to navigate the burgeoning scientific landscape. The paper emphasizes the significance of a systematic 10-step approach, providing a framework that ensures reliability, rigor, and transparency. Conclusion: By adhering to robust protocols and reporting guidelines, nursing researchers can effectively contribute to evidence-based healthcare practices, guide policymakers, and inspire future research, ultimately closing the knowledge translation gap.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".