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.

Data-Driven Insights into Non-Purchasing Behaviours through Latent Dirichlet Allocation: Analysing Study Material Acquisition Among University Students

“…This method is influential because of its ability to systematically unearth insights that traditional analysis might overlook. Dieckmann et al (2022) portrayed topic modelling as an automated means of theme identification within a body of text. This systematised analysis streamlines the interpretation of qualitative data, which enables the revelation of hidden patterns and relationships.…”

In Search of Nursing Science

“…That is a broad array that still may not encompass all that we do and seems to reflect more about science than Science. Moreover, although their purpose was to examine dissertation topics among nursing PhDs who had completed online versus in-person or in hybrid programs, the findings of Dieckmann et al (2022) reflect, at least partly, the science of nurse scientist mentors. Interestingly, between 2015 and 2019, most dissertation topics were of a clinical focus (e.g., sleep, chronic illness management, aging, perinatal care, and mental health).…”