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Record W2809639468 · doi:10.5539/ijel.v8n5p205

English for Doctors: An ESP Approach to Needs Analysis and Course Design For Medical Students

2018· article· en· W2809639468 on OpenAlexvenueno aff
Muhammad Arfan Lodhi, Mahwish Shamim, Mahwish Robab, Syed Khuram Shahzad, Abida Ashraf

Bibliographic record

VenueInternational Journal of English Linguistics · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsMedical educationNeeds analysisEnglish for specific purposesEnglish languagePsychologyCommunicative language teachingMedical professionSurvey researchCourse (navigation)Mathematics educationMedicineLanguage educationEngineeringApplied psychology

Abstract

fetched live from OpenAlex

English is considered a language of communication as well as language of fashion and status in Pakistan. For last some decades, it is being taught in different institutions to meet specified academic and professional needs of learners. As far as medical profession is concerned, doctors need English language during their academic studies as well as in their professional settings. The study investigated the communicative needs of doctors at academic and professional level in survey based research. The data was collected, analyzed and interpreted quantitatively by administering questionnaire among medical students and doctors. The findings revealed that there is huge gap between the acquired competencies of doctors with their desired level of English proficiency skills. Majority of the respondents stressed upon the need to introduce English language courses and workshops for medical students and doctors respectively, so that they may fulfill their communicative needs in effective way.

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)0.002
metaresearch head score (Gemma)0.057
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.902
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.057
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.348
Teacher spread0.305 · 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 designNot applicable
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

Citations31
Published2018
Admission routes1
Has abstractyes

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