Medical Theses and Derivative Articles: Dissemination Of Contents and Publication Patterns

TL;DR

This study analyzes how derivative articles from medical theses can be identified through text similarity, revealing patterns in authorship, publication timing, and the role of supervisors.

Contribution

It introduces a text analysis methodology using full-text similarity, particularly in the Discussion section, to distinguish derivative articles from non-derivative ones.

Findings

Discussion section similarity rate discriminates derivative articles

Supervisors are coauthors in 100% of derivative articles

87.5% of derivative articles published before or in the same year as thesis completion

Abstract

Doctoral theses are an important source of publication in universities, although little research has been carried out on the publications resulting from theses, on so-called derivative articles. This study investigates how derivative articles can be identified through a text analysis based on the full-text of a set of medical theses and the full-text of articles, with which they shared authorship. The text similarity analysis methodology applied consisted in exploiting the full-text articles according to organization of scientific discourse (IMRaD) using the TurnItIn plagiarism tool. The study found that the text similarity rate in the Discussion section can be used to discriminate derivative articles from non-derivative articles. Additional findings were: the first position of the thesis's author dominated in 85% of derivative articles, the participation of supervisors as coauthors occurred in 100% of derivative articles, the authorship credit retained by the thesis's author was 42% in derivative articles, the number of coauthors by article was 5 in derivative articles versus 6.4 coauthors, as average, in non-derivative articles and the time differential regarding the year of thesis completion showed that 87.5% of derivative articles were published before or in the same year of thesis completion.