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Record W3167052848 · doi:10.17507/tpls.1106.05

English for General Academic Purposes or English for Specific Purposes? Language Learning Needs of Medical Students at a Chinese University

2021· article· en· W3167052848 on OpenAlexfundno aff
Yuehua Li, Marion Heron

Bibliographic record

VenueTheory and Practice in Language Studies · 2021
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsnot available
FundersEducation Department of Henan ProvinceUniversity of SurreyChina Scholarship CouncilRyerson University
KeywordsPerceptionMedical educationCurriculumNeeds analysisPsychologyChinaEnglish for specific purposesEnglish languageMathematics educationPedagogyMedicinePolitical science

Abstract

fetched live from OpenAlex

The debate over the appropriacy of EGAP and ESP has been an ongoing concern in many higher education contexts. In this paper we discuss how teachers’ and students’ perceptions of English curriculum needs are reflected in the conflict between short-term goals, such as passing exams, and long-term goals, such as career development. Students, doctors and teachers at a medical university in the central part of China were asked about their needs through questionnaires and structured interviews. The findings suggest that whilst many felt the need for medical English to be taught in the early years, particularly through medical texts, there was also push back due to the need for general English to pass English exams, such as CET4/6. We argue that through the incorporation of medical texts, students can start to develop their medical English from the first year of university. This not only ensures the motivation for students to study medical English for professional purposes, but also fulfills the perceived need to prepare for the exam.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.061
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.947

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.061
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.063
GPT teacher head0.499
Teacher spread0.436 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2021
Admission routes1
Has abstractyes

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