Needs Analysis of Chilean Students of Dentistry for Dental English Course Development
Bibliographic record
Abstract
The purpose of the study is to determine the necessity for a Dental English course in the dentistry degree program at the Universidad Autónoma de Chile. To achieve this objective, the target needs analysis instrument developed by Hutchinson and Water (1987) was adapted and distributed to 91 dentistry students and 35 of their teachers in order to determine the students' necessities, lacks, wants, expectations, preferred style of learning and teaching, and interest in the English for Dentistry course. For the quantitative data analysis, descriptive statistics were utilized, while thematic analysis was adopted for the qualitative data analysis. The results indicated the perceived importance of English is related to studying dentistry in their program and practicing dentistry in their future careers as dentists, the sense of lack is demonstrated in the inadequacy of English learning courses in their program curriculum as well as their current low level of English proficiency, and the need to focus on reading, writing, and vocabulary skills in order to write and read research articles in English. The study recommends implementation of a comprehensive branch of English language instruction that include English for General Purposes and English for Specific Purposes, such as English for Dentistry, English for Dental Consultation, and English for Writing Research Articles.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".