The Impact of Stakeholders on Health Services Development: An Empirical Investigation on the Surgical Department at King Fahd General Hospital, Saudi Arabia
Bibliographic record
Abstract
The current research investigates the stakeholders influencing health services development at King Fahd General Hospital KFGH in Jeddah city, Saudi Arabia. This study proposes and tests a six factors model that influences health services development. These factors include government regulations, competitors, suppliers, patients, public, and health service providers as independent variables; the dependent variable is health services development. In order to explore this issue, a quantitative method was used to collect primary data through a questionnaire, which was administered in KFGH in Jeddah city in Kingdom of Saudi Arabia. The researches targeted 141 surgeons in this research as a sample because of the small population. A purposive sample was used to choose the participants in this research. The research retrieves 130 valid questionnaires; representing 92%.The results confirm significant differences in the influence of these factors on health service development. The research concludes that there is a significant influence of governmental regulations, competitors, suppliers, patients, public, and health service providers on health services development. The research recommends enhancing the awareness of stakeholder factors by studying the effects of governmental regulations, competitors, suppliers, patients, public, and health service providers. The last is adopting and updating medical and non-medical technology to maintain health service development.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".