SynthesisJournal of advanced nursing2014
The process and utility of classification and regression tree methodology in nursing research.
Synthesis in Journal of advanced nursing, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Clinical nurses' work-life balance prediction due to patient safety incidents using classification and regression tree analysis: a secondary data analysis.BMC nursing · 2024Article
- Low level of BRCA2 in peripheral blood lymphocytes is associated with an increased risk for head and neck squamous cell carcinoma (HNSCC) in a population of North-East India: a case-control study.Ecancermedicalscience · 2023Article
- Ranked determinants of telemedicine diabetic retinopathy screening performance in the United States primary care safety-net setting: an exploratory CART analysis.BMC health services research · 2022Article
- A role for community-level socioeconomic indicators in targeting tuberculosis screening interventions.Scientific reports · 2022Article
- Prediction Tool to Identify Children at Highest Risk of Tuberculosis Disease Progression Among Those Exposed at Home.Open forum infectious diseases · 2021Article
- Patient Risk Factor Profiles Associated With the Timing of Goals-of-Care Consultation Before Death: A Classification and Regression Tree Analysis.The American journal of hospice & palliative care · 2020Observational
- Using classification and regression tree analysis to explore parental influenza vaccine decisions.Vaccine · 2020Article
- Temporal validation of the CT-PIRP prognostic model for mortality and renal replacement therapy initiation in chronic kidney disease patients.BMC nephrology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimThis paper presents a discussion of classification and regression tree analysis and its utility in nursing research.
backgroundClassification and regression tree analysis is an exploratory research method used to illustrate associations between variables not suited to traditional regression analysis. Complex interactions are demonstrated between covariates and variables of interest in inverted tree diagrams.
designDiscussion paper. DATA SOURCES: English language literature was sourced from eBooks, Medline Complete and CINAHL Plus databases, Google and Google Scholar, hard copy research texts and retrieved reference lists for terms including classification and regression tree* and derivatives and recursive partitioning from 1984-2013. DISCUSSION: Classification and regression tree analysis is an important method used to identify previously unknown patterns amongst data. Whilst there are several reasons to embrace this method as a means of exploratory quantitative research, issues regarding quality of data as well as the usefulness and validity of the findings should be considered. IMPLICATIONS FOR NURSING RESEARCH: Classification and regression tree analysis is a valuable tool to guide nurses to reduce gaps in the application of evidence to practice. With the ever-expanding availability of data, it is important that nurses understand the utility and limitations of the research method.
conclusionClassification and regression tree analysis is an easily interpreted method for modelling interactions between health-related variables that would otherwise remain obscured. Knowledge is presented graphically, providing insightful understanding of complex and hierarchical relationships in an accessible and useful way to nursing and other health professions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.