Exploring Factors Influencing English-Speaking Skills among Saudi EFL Security Guards in Healthcare Institutions
Bibliographic record
Abstract
The aim of this study was to investigate the perceived factors contributing to the English-speaking challenges facing Saudi EFL security guards in health institutions. The data were gathered via questionnaires completed by 258 security guard students (132 males and 126 females) and interviews with 6 English teachers (3 males and 3 females). The findings indicated that course content and materials, as well as teaching methods, contribute to the speaking problems encountered by Saudi EFL security guards in health institutions. Concerns with course content and resources include insufficient content time, inadequate tasks for English language practice, and a lack of relevance to students' language learning needs. Regarding teaching methods, the study demonstrates the influence of institutional power, time constraints, and content-heavy programs that involve teaching methods, such as the Grammar Translation Method (GTM). This method, which prioritizes memorizing isolated vocabulary rather than fostering communicative language skills in many relevant settings, has contributed to the students’ speaking challenges. The study's implications and limitations were discussed.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".