Connecting patients with clinical trials using patient navigation: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Interventions are needed to increase participation in clinical trials through optimised trial design and enrolment workflows. Patient navigation is a promising intervention for increasing participation in clinical trials by optimising enrolment workflows. Patient navigation is defined as a personalised intervention aimed at overcoming barriers and ensuring timely access to healthcare services, diagnosis, treatment and care. This scoping review aims to fill a gap in current literature by summarising what is known about patient navigation, aiming to increase clinical trial participation. METHODS AND ANALYSIS: A search was conducted for peer-reviewed literature published in English from inception through 21 December 2023, and the search was updated on 5 March 2025. Sources of literature included Cochrane CENTRAL (Ovid), MEDLINE (Ovid), EMBASE (Ovid), Cumulative Index of Nursing and Allied Health (CINAHL; on EBSCOhost; EBSCO Industries, Inc), Epistemonikos and PROSPERO databases. Searches were also conducted through the Turning Research into Practice and International Clinical Trials Registry Platform (WHO) databases, Google Scholar and the Agency for Health Research and Quality platform to ensure the retrieval of all relevant articles. Reference lists of eligible studies were also examined. The Google Scholar search was limited to the first 10 pages of results. The search strategy focused on the following key concepts: navigation (eg, navigator, care coordination, case management) and clinical trials. Searches were reviewed using the PRESS Peer Review of Electronic Search Strategies 2015. This review was guided based on the JBI methodology for scoping reviews using a five-step review process: identify the research questions; search and identify relevant studies; select studies based on a priori criterion; chart the data; and collate, summarise and report the results according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews. ETHICS AND DISSEMINATION: This scoping review identifies and analyses existing research; therefore, ethics approval is not required. Findings will be disseminated through conference presentations and a publication in a scientific journal.
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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 | 0.127 | 0.105 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.016 | 0.014 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.085 | 0.021 |
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".