Enhancing Security Service Efficiency Through Risk Management: Analyzing the Impact of Employee Leave Patterns at King Saud bin Abdulaziz University for Health Sciences
Bibliographic record
Abstract
Security service efficiency in higher education institutions depends critically on workforce availability, yet the relationship between employee leave patterns and operational performance remains understudied.This study investigates this relationship at King Saud bin Abdulaziz University for Health Sciences through mixed-methods research combining quantitative analysis of 2,363 leave instances (2022-2024), surveys of 160 stakeholders, and semi-structured interviews with 15 security managers.Temporal analysis identified pronounced leave clustering on weekends (45.9%) and during January, August, and November.Correlation analysis revealed significant relationships between absence rates and security coverage (r = -0.71,p < 0.001), incident response times (r = 0.74, p < 0.001), and incident frequency (r = 0.68, p < 0.01).Monte Carlo simulations of three risk mitigation scenarios demonstrated that comprehensive leave management could reduce response times by 37.4% and incidents by 48.6%, with projected annual net benefits of SAR 330,000.Findings establish absence management as a critical component of security risk management in educational institutions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".