Nursing students' perception of their clinical practice in intensive care units: A study from Egypt
Bibliographic record
Abstract
Providing nursing students with quality clinical experience in intensive care units (ICUs) is a major concern for nursing educators in Egypt. Understanding nursing students' perception of their critical care experience is important in future planning of successful clinical placements in ICUs. The purpose of this study was to investigate undergraduate nursing students' perception of their clinical practice in ICUs. The study involved 306 nursing students who were registered in critical care nursing course. Data were collected using a self-administered survey which addressed nursing students' perception of three domains including clinical practice environment, clinical teaching and learning and factors hindering clinical practice in intensive care setting. The results illustrated that the majority of students enjoyed their clinical experience in ICUs. However, students highlighted many factors that hindered their clinical practice such as the stressful intensive care setting, fear of making mistakes, complex patients’ conditions, theory-practice gap, overburdening with documentation and lack of coordination between clinical placements. Supportive learning environment is needed to enhance students' clinical learning, improve collaboration between students, demonstrators and critical care nursing staff, and reduce theory-practice gap.
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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 | 0.002 |
| 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 | 0.001 | 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".