Factors Influencing Saudi Nurses Turnover "Empirical Study In Ministry Of Health Hospitals-Jeddah City
Bibliographic record
Abstract
Health systems around the world is facing a lot of growing challenges day after day. The most important challenge is increasing health needs, beside lack of physical and human funding. On the other hand, on the other hand, the whole world has faced a growing shortage of nursing staff because of different and altered reasons from one country to another. Historically, nursing profession is a female career as most of the workers in this profession are women more than men. In Saudi society, nursing profession has faced many challenges and issues, and it was one of the most unjustly professions in the community because of society's perception, atavism tradition, so a big number of nurses would reluctance to continue in the profession for either work- related reasons or personal reasons, in other words nursing turnover. Although nursing shortage is considered a global problem but this problem appears to be the most significant problem in Saudi Arabia. This not only because of shortage in nursing staff but also shortage in local nursing staff. Exposure to the problems of nursing is repeatable either as work environment problems or personal problems. Whilst the personal problem could be different between eastern society and western, country to other and even from one individual to other. Society and social relation in eastern country usually impacted on individual and particularly on health practitioners because of their opened work environment in closed society. by focusing and studying such problem in Saudi Arabia, it would to assist in finding solutions for nursing problems and reduce turnover rate. This research will explore the problem of Saudi nursing turnover and how factors such family obligations, guardian's decision, society perception and demographic variables affecting nurses continuity in their profession.
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 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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".