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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".