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The interfaces between the fields of communication, education and health have been indicated by international institutions such as the WHO and the European Centre for Disease Prevention and Control. However, hegemonic scientific practices supersede dialogue between the three fields, isolating their practices. This fragmenting tendency is observed in scientific literature, which has created gaps in the dialogue and articulation between communication, education and health. Although health promotion requires both communicative and educational practices, the epistemological, historical, political, cultural and socioeconomic aspects have also engendered tensions between the fields. Communication is often seen as a mere instrument for other practices, rather than a phenomenon that (re)produces meanings and power dynamics. In opposing the reductionist and instrumentalising perspectives of knowledge fields, the primary objective of the scoping review is to map the scientific evidence on the interfaces between communication and education in health to indicate a conceptual framework that articulates communication and education practices within the context of health.
A transdisciplinary team developed this protocol based on the 2024 Joanna Briggs Institute Manual for Evidence Synthesis. The procedures required to conduct the review were guided by the frameworks proposed by Arksey and O'Malley, Levac et al and Peters et al. The study eligibility criteria were established based on the Problem, Concept and Context outlined in the research questions. Primary and secondary studies will be retrieved from nine sources, covering both conventional and grey literature. These sources include Embase, ERIC, LILACS, PubMed/MEDLINE, ScienceDirect, Scopus, Web of Science, the Brazilian Digital Library of Theses and Dissertations, and the Networked Digital Library of Theses and Dissertations. A categorised form will be used for data collection and subsequent analysis. The reporting of the review findings will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews.
The nature of the research and the use of secondary data sources do not require informed consent forms or approval from ethics committees in Brazil. The scientific findings from the review will be disseminated through peer-reviewed journals, academic conferences and other scientific communication channels.
The protocol was registered on the Open Science Framework (OSF) and is available at https://doi.org/10.17605/OSF.IO/Z3CX7.
To develop and validate decision trees using conditional probabilities to identify the predictors of mortality and morbidity deterioration in trauma patients.
A quasi-experimental longitudinal study conducted at a Level 1 Trauma Center in São Paulo, Brazil.
The study analysed 201 patient records using standardised nursing documentation (NANDA International and Nursing Outcomes Classification). Decision trees were constructed using the chi-squared automatic interaction detection (CHAID) algorithm and validated through K-fold cross-validation to ensure model reliability.
Decision trees identified key predictors of survival and mobility deterioration. Patients who did not require (NOC 0414) Cardiopulmonary Status but required (NOC 0210) Transfer Performance had a 97.4% survival rate. Conversely, those requiring (NOC 0414) Cardiopulmonary Status had a 25% risk of worsening mobility, compared to 9% for those who did not. K-fold cross-validation confirmed the model's predictive accuracy, reinforcing the robustness of the decision tree approach (Value).
Decision trees demonstrated strong predictive capabilities for mobility outcomes and mortality risk, offering a structured, data-driven framework for clinical decision-making. These findings underscore the importance of early mobilisation, tailored rehabilitation interventions and assistive devices in improving patient recovery. This study is among the first to apply decision trees in this context, highlighting its novelty and potential to enhance trauma critical care practices.
This study highlights the potential of decision trees, a supervised machine learning method, in nursing practice by providing clear, evidence-based guidance for clinical decision-making. By enabling early identification of high-risk patients, decision trees facilitate timely interventions, reduce complications and support personalised rehabilitation strategies that enhance patient safety and recovery.
This research addresses the challenge of improving outcomes for critically ill and trauma patients with impaired mobility by identifying effective strategies for early mobilisation and rehabilitation. The integration of artificial intelligence-driven decision trees strengthens evidence-based nursing practice, enhances patient education and informs scalable interventions that reduce trauma-related complications. These findings have implications for healthcare providers, rehabilitation specialists and policymakers seeking to optimise trauma care and improve long-term patient outcomes.
Patients provided authorisation for the collection of their clinical data from medical records during hospitalisation.
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