English Usage and Problems of Industrial Pharmacists at Two Large Multi-National Pharmaceutical Manufacturers in Thailand
Bibliographic record
Abstract
In pharmaceutical industry, insufficient English proficiency of industrial pharmacists in international communication can cause adverse outcomes in the process of overseas product registration and regulatory audits. This study explores English use and problems of 51 industrial pharmacists within two large multinational pharmaceutical manufacturers by using a self-developed questionnaire based on the frameworks of needs analysis (Hutchinson & Waters, 1987) and communicative competence (Canale & Swain, 1980). The findings indicate that reading is the most frequently used skill, followed by writing, listening and speaking, respectively. Specific communicative tasks which Thai industrial pharmacists most commonly performed were: 1) reading emails, 2) writing emails, 3) reading validation protocols/reports, 4) reading pharmacopoeias and pharmaceutical textbooks, and 5) reading procedural documents. A major problem lies within oral communication skills. The implications of the findings show valuable sources of target language events which can benefit ESP educators and pharmaceutical trainers in the development of ESP courses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".