Nursing Education Challenges from Saudi Nurse Educators’ and Leaders’ Perspectives: A Qualitative Descriptive Study
Bibliographic record
Abstract
Aim To explore the challenges in nursing education in Saudi Arabia from nurse educators' and leaders' perspectives. Background Saudi Arabia considerably lacks Saudi nurses and most nurses in Saudi healthcare facilities are expatriates with diverse academic, cultural, and linguistic backgrounds. The perception regarding nurses' profession and their low status in Saudi Arabia affect the nursing profession negatively, leading to staff insufficiency. Understanding the difficulties of the nursing profession could help intervene accordingly, in encouraging students to pursue a career in nursing. Methods An exploratory qualitative method was used. Three Saudi nursing educators and four leaders were interviewed. They were recruited through purposive sampling. The interviews were transcribed verbatim and data were analyzed using thematic analysis. Findings Participants indicated that nursing education in Saudi Arabia is facing multiple challenges requiring serious attention from Saudi leaders. The challenges were divided to four themes and different categories; cultural challenges, educational challenges, organizational challenges (weak nursing authority, lack of acknowledgement for Saudi nurses), and work challenges (poor working environment, language barrier). Discussion The challenges that face nursing education in Saudi Arabia were mainly at three levels: management, system, and social. These challenges were the reasons for Saudi nurses to leave the profession. Conclusion This study revealed some of the challenges facing nursing education in Saudi Arabia. These challenges are not unique to Saudi Arabia but knowing about these challenges will help nurse educators and leaders to improve nursing programs, enhance students' clinical experiences, and promote nursing profession in Saudi Arabia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".