Graduation Year

2005

Document Type

Dissertation

Degree

Ph.D.

Degree Granting Department

Nursing

Major Professor

Mary E. Evans, Ph.D.

Committee Member

Patricia Burns, Ph.D.

Committee Member

Arthur Shapiro, Ph.D.

Committee Member

Jason Beckstead, Ph.D.

Keywords

coding scheme, classification systems, Delphi, focus group, nursing database, nursing data element, nursing education, online survey, ontology, taxonomy, unified nursing language

Abstract

Globally, health care professionals, administrators, educators, researchers, and informatics experts have found that minimum dataset and taxonomies can solve the problem of data standardization required in building an information system to advance disciplines body of knowledge. Disciplines continuously gather complex data, but data collected without an organizational context does not increase the knowledge-base. Therefore, a demand exists for developing minimum dataset, controlled vocabularies, taxonomies, and classification systems. To fulfill nursings needs for standardized comparable data, two minimum dataset are used in nursing for organizing, classifying, processing, and managing information for decision-making and advancing clinical nursing knowledge.

No minimum dataset in nursing education currently exists. With common definitions and taxonomy of nomenclature related to nursing education, research findings on similar topics can aggregate data across studies and settings to observe overall patterns. Understanding patterns will allow educators, researchers, and administrators to interpret and compare findings, facilitate evidence-based changes, and draw significant conclusions about nursing education programs, schools, and educational experiences.

This study proposes a generic methodology for building a Nursing Education Minimum Dataset (NEMDS) by exploring experiences of developing various minimum dataset. This study adapted the systems model as the conceptual framework for building the taxonomy and classification system of nursing education essential data elements to guide the analysis of structure, process, and outcome in nursing education. The study suggested using focus groups, an online Delphi survey, and the statistical techniques of Multidimensional Scaling, and kappa. The study presented these steps: identifying educational concepts and data elements; defining data elements as nursing education terminologies; building the taxonomy; conducting an empirical and theoretical validation; disseminating and aggregating the data in national dataset.

The proposed methodology to build an NEMDS meets the criteria of having a nursing education dataset that is mutually exclusive, exhaustive, and consistent with the concepts that help nursing educators and researchers to describe, explain, and predict outcomes in the discipline of nursing education. It can help the transformation of simple information into a meaningful knowledge that can be used and compared by the school, state or country to advance nursing education research and practice nationally or internationally.

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