Building the Medical Lexicon: A Corpus-Based Approach to Optimising Medical Terminology Acquisition for Pre-Health Science Students

Authors

  • Amira Abdullah Alshehri

    Department of Languages and Translation, College of Education and Arts, University of Tabuk, Tabuk 47512, Saudi Arabia

DOI:

https://doi.org/10.30564/fls.v6i6.7400
Received: 2 October 2024 | Revised: 29 October 2024 | Accepted: 30 October 2024 | Published Online: 9 December 2024

Abstract

Pre-health science students, especially those learning English as a Foreign Language (EFL), encounter substantial challenges in acquiring medical vocabulary, as they must master specialised terminology while developing general English proficiency. This study addresses this issue by proposing a frequency-based, corpus-driven approach to streamline medical vocabulary acquisition and reduce cognitive load. Focusing on the skeletal system chapter of a medical textbook, the research categorises terms into four groups—foundational, intermediate, advanced, and deferred—based on their frequency and relevance within medical discourse. Using Sketch Engine as the primary corpus tool for analysis, high-frequency terms are prioritised in early instruction to build a strong foundation, while more complex terms are introduced incrementally to support progressive knowledge development. Rare, highly technical terms are deferred to advanced stages, ensuring students engage with essential terminology at appropriate learning points. The study provides a practical, data-driven framework adaptable across other medical domains, offering a scalable model to enhance EFL students’ vocabulary acquisition. By aligning instruction with frequency-based categories, educators can better manage students' learning burden, promote retention, and ensure mastery of critical concepts, while also guiding curriculum planning to foster gradual and structured learning progression.

Keywords:

Lexicon; Frequency-Based Approach; Medical Terminology; Vocabulary Acquisition; Corpus Analysis

References

[1] Ismayilli-Karakoç, A., 2020. Teaching medical terminology to speakers of English as a foreign language. In: Genç ZS. and Kaçar, IG. (Eds.), TESOL in the 21st Century: Challenges and opportunities. Peter Lang: Bristol, UK. pp. 235-252.

[2] Panocová, R., 2017. The Vocabulary of Medical English: A Corpus-Based Study. Cambridge Scholars Publishing: Newcastle Upon Tyne, UK. pp. 1-190.

[3] Kalyuga, M., Kalyuga, S., 2008. Metaphor awareness in teaching vocabulary. Language Learning Journal. 36(2), 249-257. DOI: https://doi.org/10.1080/09571730802390767

[4] Webb, S., Nation, P., 2017. How Vocabulary is Learned. Oxford University Press: Oxford, UK. pp. 1-336.

[5] Wang, X., Reynolds, B. L., 2024. Beyond the books: Exploring factors shaping Chinese English learners’ engagement with large language models for vocabulary learning. Education Sciences. 14(5), 496.

[6] Sun, W., Park, E., 2023. EFL Learners’ Collocation Acquisition and Learning in Corpus-Based Instruction: A Systematic Review. Sustainability. 15(17), 13242.

[7] Quero, B., Coxhead, A., 2018. Using a corpus-based approach to select medical vocabulary for an ESP course: The case for high-frequency vocabulary. In: Kırkgöz, Y. and Dikilitaş, K. (Eds.). Key Issues in English for Specific Purposes in Higher Education. Springer International Publishing: Cham, Switzerland. pp. 51-75.

[8] Chen, Q., Ge, G. C., 2007. A corpus-based lexical study on frequency and distribution of Coxhead’s AWL word families in medical research articles (RAs). English for Specific Purposes. 26(4), 502-514. DOI: https://doi.org/10.1016/j.esp.2007.04.003

[9] Dang, T. N. Y., Webb, S., 2014. The lexical profile of academic spoken English. English for Specific Purposes. 33, 66-76. DOI: https://doi.org/10.1016/j.esp.2013.08.001

[10] Le, N. H., Ha, H. T., 2023. Lexical demands of academic written English: From students’ assignments to scholarly publications. Sage Open. 13(4), 1-16. DOI: https://doi.org/10.1177/21582440231216292

[11] Hsu, W., 2013. Bridging the vocabulary gap for EFL medical undergraduates: The establishment of a medical word list. Language Teaching Research. 17(4), 454-484. DOI: https://doi.org/10.1177/1362168813494121

[12] González-Fernández, B., Schmitt, N., 2017. Vocabulary acquisition. In: Sato, M and Loewen, S. (Eds.). The Routledge Handbook of Instructed Second Language Acquisition. Taylor & Francis: London, UK. pp. 280-298.

[13] Schmitt, N., 1998. Tracking the incremental acquisition of second language vocabulary: A longitudinal study. Language Learning. 48(2), 281-317.

[14] Schmitt, N., 2019. Understanding vocabulary acquisition, instruction, and assessment: A research agenda. Language Teaching. 52(2), 261-274.

[15] Najafi, M., Talebinezhad, M. R., 2018. The impact of teaching EFL medical vocabulary through collocations on vocabulary retention of EFL medical students. Advances in Language and Literary Studies. 9(5), 24-27. DOI: http://doi.org/10.7575/aiac.alls.v.9n.5p.24

[16] Coxhead, A., 2014. Vocabulary and ESP. In: Paltridge, B., Starfield, S. (Eds.). The Handbook of English for Specific Purposes. Wiley-Blackwell: Berlin, Germany. pp. 115–132.

[17] Liu, D., Lei, L., 2019. Technical vocabulary. In: Webb, S. (Ed.). The Routledge Handbook of Vocabulary Studies. Taylor & Francis: London, UK. pp. 111–124

[18] Barclay, S., Pellicer-Sánchez, A., 2021. Exploring the learning burden and decay of foreign language vocabulary knowledge: The effect of part of speech and word length. ITL-International Journal of Applied Linguistics. 172(2), 259-289. DOI: https://doi.org/10.1075/itl.20011.bar

[19] Ehrlich, A., Schroeder, C. L., Ehrlich, L., et al., 2021. Medical Terminology for Health Professions. Delmar Cengage Learning: Boston, United States. pp. 1-672.

[20] Kilgarriff, A., Baisa, V., Bušta, J., et al., 2014. The Sketch Engine: ten years on. Lexicography. 1(1), 7-36. DOI: https://doi.org/10.1007/s40607-014-0009-9

[21] Le, C. N. N., Miller, J., 2020. A corpus-based list of commonly used English medical morphemes for students learning English for specific purposes. English for Specific Purposes. 58, 102-121. DOI: https://doi.org/10.1016/j.esp.2020.01.004

[22] Leech, G., Rayson, P., Wilson, A., 2014. Word Frequencies in Written and Spoken English: Based on the British National Corpus, 2nd ed. Taylor & Francis: London, UK. pp 1-320.

[23] Cohen, J., 2013. Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Taylor & Francis: London, UK. pp. 1-567.

[24] Wright, B. D., Masters, G. N., 1982. Rating Scale Analysis. MESA Press: Chicago, IL, USA. pp. 1-206.

[25] Hinton, P. R., 1995. Statistics explained: a guide for social science students. Routledge: London, UK. pp. 1-322.

[26] Biber, D., Conrad, S., Reppen, R., 2004. Corpus Linguistics: Investigating Language Structure and Use, 4th ed. Cambridge University Press: Cambridge, UK. pp. 1-340.

[27] Manning, C. D., Schütze, H., 1999. Foundations of Statistical Natural Language Processing. MIT Pres: Cambridge, United States. pp. 1-720.

[28] Drewry, H., Notterman, J., 2013. Psychology and Education: Parallel and Interactive Approaches. Springer: New York, United States. pp. 1-290.

[29] Baker, P., 2023. Using Corpora in Discourse Analysis, 2nd ed. Bloomsbury Publishing: London, UK. pp. 1-280.

[30] Bergman, E.M., de Bruin, A.B., Herrler, A., et al., 2013. Students’ perceptions of anatomy across the undergraduate problem-based learning medical curriculum: a phenomenographical study. BMC Med Educ. 13, 152.

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How to Cite

Abdullah Alshehri, A. (2024). Building the Medical Lexicon: A Corpus-Based Approach to Optimising Medical Terminology Acquisition for Pre-Health Science Students. Forum for Linguistic Studies, 6(6), 558–574. https://doi.org/10.30564/fls.v6i6.7400

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