An ESP Approach to Teaching Nursing Students the Quality of Clinical Nursing Notes Writing
Bibliographic record
Abstract
For nursing students in the education system in Saudi Arabia, English-language writing skills, whether for general or specific purposes, have long been ignored, which may jeopardize their success in medical-oriented courses and their future careers. This study designed clinical nursing note-writing course (CNNWC) for university nursing students and explored the teaching outcomes of its implementation. The three main objectives were to (a) examine the effectiveness of the CNNWC in enhancing learners’ competencies; (b) survey learners’ satisfaction with the CNNWC, and (c) investigate learners’ perceptions of the CNNWC. In this action research, 47 students practiced four writing tasks while guided with four teaching tools, namely, multiple revisions, peer review activities, and direct and indirect teacher feedback, for a semester. External examiners included a language teacher and a nursing professional, and the data-collection instruments used included a writing competence scale and a course satisfaction questionnaire. The results showed that the learners’ writing competence significantly improved after the CNNWC. They also demonstrated a fair level of satisfaction toward the CNNWC. The learners indicated a preference for feedback from the teacher rather than from peers, and they perceived vocabulary capability to be crucial. ESP/ENP teachers are advised to consider the implementation of the CNNWC when designing syllabi.
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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".