Unravelling Medical Results: A Corpus-Driven Exploration of Lexical Bundles, Lexical Frames, and Rhetorical Moves

Abstract

Publishing in international medical journals poses a significant challenge for non-native English-speaking researchers, particularly in Results sections that conform to a discourse community. However, previous studies have examined lexical bundles (LBs) and lexical frames (LFs) separately and did not explore the connection to rhetorical move structures. This corpus-driven study explored the lexical bundles and frames in results sections of medical research articles, analysed their structural types and mapped them to move structures. Python scripts extracted 5-word spans of LBs and LFs from 3,000 research articles in a four-million-word corpus named RESMED with a frequency threshold of 80 occurrences, and a range criterion of 80% across five high-impact medical journals. Concordance line analysis was used to determine their structural patterns and move structure mapping. Results revealed 67 types of lexical bundles and 225 types of lexical frames predominantly related to the medical trial subjects and statistical data. Structurally, most lexical bundles were noun phrase-based, and a new type explicative with copula be emerged from the analysis. Lexical frames frequently occurred with non-verb content words, followed by verb-based frames and frames with function words. Move structure mapping showed that most lexical bundles and frames appeared in Move 3: Describe statistical test and state findings and Move 1: Report the findings, with fewer occurrences in Move 2: Review the experiment and Move 4: Summary report of the overarching findings. These findings reveal how medical writers construct results sections through FSs in specific moves and steps, offering pedagogical implications.