Predictors of Academic and Clinical Stress Among Nursing Students
Bibliographic record
Abstract
Introduction: Nursing students experience significant academic and clinical stress due to challenging coursework, assessments, and educational demands, as well as the pressures associated with gaining practical healthcare experience. These challenges can lead to anxiety, fear of making errors, and concerns about patient well-being, ultimately impacting students' mental health and their preparation for a nursing career. Objective: assesses the predictors of academic and clinical stress among nursing students. Method: A cross-sectional study design was used with a convenience sample of 113 nursing students. The study team conducted the study at the Faculty of Applied Medical Science, Nursing Department at Prince Sattam bin Abdulaziz University. The researchers used three instruments for data collection and asked all nurse students to complete the online questionnaires, which utilized perceived clinical settings and academic stress. Results: The mean age of the studied nursing students was 21.2 years old. The study observed students had moderate perceived stress levels furthermore, the total levels of the academic stress domain's mean and standard deviation in students were 90.5 ± 32. Also, a significant relationship between perceived stress and clinical setting hours per week in nursing students, and hospital clinical setting type in nursing students. There was a correlation between academic stress and the ages of students. Students' academic stress was statistically significantly correlated with their educational level. Conclusion: Academic and clinical stress significantly impacts nursing students; the most common reasons that increase stress are fear of making mistakes and harming the patient. Recommendation: Accordingly, the study advocates for developing educational strategies and a structured course curriculum before student nurses are sent to clinical areas to enhance their capabilities and reduce academic and clinical stress.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".