Expatriate health professionals in the Saudi Arabia private sector
Bibliographic record
Abstract
This study aimed to identify the differences in job satisfaction levels among expatriate health professionals (EHPs) working in the Saudi Arabian private sector, the stressors affecting their job satisfaction, and the influence of those stressors on their turnover intention. A cross-sectional design was guided by Herzberg's Theory. A convenience sample of 204 expatriate doctors, nurses, and pharmacists in Saudi Arabia were recruited from the private sector. Data were collected from four urban hospitals in regions having 74% of the bed capacity in the private sector. A four-part instrument measured dissatisfiers, job satisfaction, turnover intention, and cultural unrest. The results showed that EHPs have moderate job satisfaction. There was a difference in job satisfaction levels in terms of profession and nationality. Stressors influencing job satisfaction support Herzberg's theory. There was a significant positive correlation between dissatisfiers and job satisfaction. Cultural unrest had a very weak positive correlation with job satisfaction. Working conditions, salary, supervision, interpersonal relationships, hospital policy and administration, and job security were significant predictors of job satisfaction. Job satisfaction and turnover intention have insignificant relationship. The private health organisations can use the findings of this study to promote job satisfaction, decrease turnover intention, and improve cultural acclimatisation
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".