Dissertation Topics in Nursing

Abstract

Background 

Few quantitative studies have documented the types of research topics most commonly employed by nursing PhD students and whether they differ by program delivery (in-person vs. online/hybrid programs).

Objectives 

We examined a large set of publicly available PhD dissertation abstracts to (a) describe the relative prevalence of different research topics and methods and (b) test whether the primary topics and methods used differed between online or hybrid and in-person PhD programs. A secondary goal was to introduce the reader to modern text-mining approaches to generate insights from a document corpus.

Methods 

Our database consisted of 2,027 dissertation abstracts published between 2015 and 2019. We used a structural topic modeling text-mining approach to explore PhD students’ research topics and methods in United States-based doctoral nursing programs.

Results 

We identified 24 different research topics representing a wide range of research activities. Most of the research topics identified did not differ in prevalence between online/hybrid and in-person programs. However, online/hybrid programs were more likely to engage students in research focused on nursing education, professional development, work environment, simulation, and qualitative analysis. Pediatrics, sleep science, older adults and aging, and chronic disease management were more prevalent topics in in-person-only programs.

Discussion 

The range of topics identified highlights the breadth of research nursing PhD students’ conduct. Both in-person and online/hybrid programs offer a range of research opportunities, although we did observe some differences in topic prevalence. These differences could be due to the nature of some types of research (e.g., research that requires an in-person presence) or differences in research intensity between programs (e.g., amount of grant funding or proximity to a medical center). Future research should explore why research topic prevalence may vary by program delivery. We hope that this text-mining application serves as an illustrative example for researchers considering how to draw inferences from large sets of text documents. We are particularly interested in seeing future work that might combine traditional qualitative approaches and large-scale text mining to leverage the advantages of each.