Twenty Years of Nurse-Led Research in Hemato-Oncology: A Mapping Review
Bibliographic record
Abstract
OBJECTIVES: Nurse-led research in hemato-oncology is diverse, but its nature and extent are unknown. This review aimed to identify and map nurse-led research in hemato-oncology over 20 years (2004-2024) to highlight under-researched gaps, describe methodological and topic trends, and allow comparison between geographical regions. METHODS: A mapping review was undertaken following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, Scoping Review (PRISMA-ScR) checklist. Five databases were systematically searched: Medline (Ovid), CINAHL (EBSCOhost), Embase (Elsevier), ProQuest, and Scopus (Elsevier). Independent screening and data extraction were undertaken on the web-based platform Covidence. RESULTS: A total of 1,916 sources were included (n = 1,618 journal publications; n = 262 published conference abstracts; n = 36 doctoral dissertations). The most common methodology was non-experimental (60.5%), followed by qualitative (19.2%), experimental (12.5%), evidence syntheses (6.3%), and mixed methods (1.5%). Most of the studies were undertaken by nurses working in the USA, followed by nurses in China, Türkiye, Canada, Australia and Iran. Studies in pediatric, adolescent, and young adult settings represented 42.4% of the included studies. A high number of studies undertaken in hematopoietic stem cell transplant settings were found. CONCLUSIONS: The number of research studies led by nurses in hemato-oncology settings, particularly in the USA, is upward. Most of the research undertaken has adopted a descriptive quantitative methodology. More interventional research is needed to contribute meaningfully to scientific knowledge that enhances the quality of care for individuals affected by blood cancer across the disease trajectory. IMPLICATIONS FOR NURSING PRACTICE: To support more nurse-led interventional research, strategic investment in mentorship, protected research time, interdisciplinary collaboration, structured clinical-academic posts, and funding pathways is needed.
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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 | 0.042 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.024 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".