Factors Affecting Surgical Delay: A Case Study of One of General Hospital at Jeddah City
Bibliographic record
Abstract
This research is a descriptive analytical study investigating the factors affecting surgical delay in the surgical department at one of general (public) hospital at Jeddah city, Saudi Arabia. The research proposes and tests four independent variable factors affecting surgical delay. These factors are: clinical, administrative, hospital capabilities and care givers, while surgical delay is the dependent variable. In order to explore this issue, a quantitative method was used to collect primary data through designing a self-administered questionnaire, which was administered at the hospital. The research targeted the surgical department’s doctors at the hospital, who are the decision makers with regards to surgeries in their specialties; they total 106 doctors, and because of the small number of the research population the total number was taken as the research sample. The research retrieved 91 valid questionnaires (96.46%). Results show that the four factors are significantly important, demonstrating a positive statistical relationship between the four factors and surgical delay. This research recommends activating the clinical coordinators’ position in all surgical departments in turn, to improve the communication channels between all the concerned departments and the patients in order to run out the patients’ appointments and surgery booking. Moreover, it is vital to frame, develop and manage all the surgical waiting lists in all surgical departments for easy access and control.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".