Pedagogical Competence of General English Teachers in Teaching English for Medical Purposes in the KSA: Myth or Reality
Bibliographic record
Abstract
This study investigates the gap between General English Teachers' (GETs) assumed ability to teach English for Medical Purposes (EMP) and their mismatched qualifications in Saudi Arabia. It explores the underdevelopment of the EMP sector, where GETs often lack proper training and resources. The research employs a qualitative case study approach, utilizing semi-structured interviews and open-ended surveys. The participants include three medical professors (MPs), five experienced EMP teachers (EMP Ts), and twenty-one GETs. The focus is on uncovering the pedagogical needs for GETs to teach EMP effectively. The findings reveal a significant discrepancy between assumed competence, preparedness and actual performance of GETs. This highlights the need for professional development programs focusing on the pedagogy of ESP and EMP, along with curriculum and instructional design, to equip GETs with the necessary skills and knowledge for successful EMP instruction. The findings also highlight the need for curriculum revision and improved needs assessment. By identifying these needs, the research aims to contribute to improved EMP teaching in Saudi Arabia. This study will assist policymakers and educators in creating a more effective system, for preparing future healthcare professionals with the essential English language skills required for success.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".