Identifying Discourse Features for Medical English Instruction: Evidence from YouTube-Based Corpus Analysis

Voice recognition and text-mining tools have democratized corpus construction, allowing educators to create responsive materials based on authentic discourse trends. In global healthcare contexts, where English functions as a lingua franca, cultivating discourse competence is essential for safe and effective communication. This study employed corpus linguistics methods to analyze authentic medical English sourced from YouTube videos. A specialized corpus was compiled and analyzed using Otter.ai and Sketch Engine to examine linguistic patterns, collocations, and discourse structures in real-world medical communication. Findings revealed that medical professionals frequently employ modal verbs (e.g., can, should, must), passive constructions, and high-frequency collocations (e.g., blood pressure, heart rate and patient care) that serve distinct pragmatic functions. Moreover, interactional phrases such as let me explain and can you see and discourse markers such as so and now are pervasive, reflecting the interpersonal and procedural nature of clinical discourse. Cross-cultural comparison further indicated notable variation in communication style, with Western contexts showing greater empathy and shared decision-making, and South Asian contexts favoring directive, authoritative tones. These patterns provide insight into the pragmatic and intercultural dimensions of medical English and highlight the potential of YouTube-based corpora as authentic resources for English for Medical Purposes (EMP) instruction.

کلیدواژه ها:

YouTube ، Discourse Analysis ، Corpus linguistics ، ESP ، Sketch Engine ، Otter.ai