Using NANDA, NIC, and NOC (NNN) Language for Clinical Reasoning With the Outcome-Present State-Test (OPT) Model

PURPOSE: To analyze the degree to which standardized nursing language was used by baccalaureate nursing students completing Outcome-Present State-Test (OPT) model worksheets in a clinical practicum. METHODS. A scoring instrument was developed and 100 worksheets were retrospectively analyzed. FINDINGS: NANDA nursing diagnoses were correctly stated in 92% of the OPT models. Nursing Outcomes Classification (NOC) outcomes were explicitly stated in 22%, and implied in 72%. Interventions matched appropriate Nursing Interventions Classification (NIC) activities in 61%. CONCLUSIONS: NANDA, NIC, and NOC (NNN) language was used inconsistently by students in this sample. IMPLICATIONS FOR PRACTICE: If NNN language is to advance nursing knowledge, its promotion, representation in curriculum development, and active use is necessary. Educational research is needed on the facilitators and barriers to NNN language use.     

The Developmental Processes for NANDA International Nursing Diagnoses

PURPOSE: This study aims to provide a step-by-step procedural guideline for the development of a nursing diagnosis that meets the necessary criteria for inclusion in the NANDA International and NNN classification systems. DATA SOURCES: The guideline is based on the processes developed by the Diagnosis Development Committee of NANDA International and includes the necessary processes for development of Actual, Wellness, Health Promotion, and Risk nursing diagnoses. DATA SYNTHESIS: Definitions of Actual, Wellness, Health Promotion, and Risk nursing diagnoses along with inclusion criteria and taxonomy rules have been incorporated into the guideline to streamline the development and review processes for submitted diagnoses. CONCLUSIONS: A step-by-step procedural guideline will assist the submitter to move efficiently and effectively through the submission process, resulting in increased submissions and enhancement of the NANDA International and NNN classification systems.