Hospital-based patient navigation programmes for patients who experience injury-related trauma and their caregivers: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Patients who experience injury-related trauma tend to have complex care needs and often require support from many different care providers. Many patients experience gaps in care while in the hospital and during transitions in care. Providing access to integrated care can improve outcomes for these patients. Patient navigation is one approach to improving the integration of care and proactively supporting patients and their caregivers as they navigate the healthcare system. The objective of this scoping review is to map the literature on the characteristics and impact of hospital-based patient navigation programmes that support patients who experience injury-related trauma and their caregivers. METHODS AND ANALYSIS: This review will be conducted in accordance with Joanna Briggs Institute methodology for scoping reviews. The review will include primary research studies, unpublished studies and evaluation reports related to patient navigation programmes for injury-related trauma in hospital settings. The databases to be searched will include CINAHL (EBSCO), EMBASE (Elsevier), ProQuest Nursing & Allied Health, PsycINFO (EBSCO) and MEDLINE (Ovid). Two independent reviewers will screen articles for relevance against the inclusion criteria. Results will be presented in a Preferred Reporting Items for Systematic Reviews and Meta-analyses for Scoping Reviews (PRISMA-ScR) flow diagram and follow the PRISMA-ScR checklist. The extracted data will be presented both tabularly and narratively. ETHICS AND DISSEMINATION: Ethics approval is not required, as the scoping review will synthesise information from publicly available material. To disseminate the findings of this review, the authors will submit the results for publication in a medical or health sciences journal, present at relevant conferences and use other knowledge translation strategies to reach diverse stakeholders (eg, host webinars, share infographics).
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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 | 0.117 | 0.081 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.015 | 0.014 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.077 | 0.018 |
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".