Assessing the Prevalence of Methicillin-Resistance Staphylococcus aureus among Healthcare Students in Madinah, Saudi Arabia
Bibliographic record
Abstract
Methicillin-resistant Staphylococcus aureus (MRSA) is a significant concern worldwide. While traditionally associated with hospitals (hospital-acquired) and other healthcare settings, MRSA infection has increasingly spread within the community. MRSA exhibits increased morbidity and mortality rates compared with other pathogens. This study aims to examine the prevalence of MRSA and its implications among healthcare students. A cross-sectional laboratory-based study was conducted from February to May 2023. A total of 149 nasal swab samples were collected from students in different medical fields at Taibah University, Madinah, Saudi Arabia. Conventional methods were used for S. aureus identification. All isolated organisms were tested for antimicrobial susceptibility against oxacillin and vancomycin. Among the 149 samples from the enrolled students, 54 (36.2%) showed isolated S. aureus. MRSA was detected in 23 (42.59%) samples with isolated S. aureus. The female students (n = 18, 42.85%) showed a higher prevalence of MRSA than did the male students (n = 5, 41.67%). All MRSA isolates were sensitive to vancomycin. The study focused on the current prevalence of MRSA among healthcare students in Madinah, Saudi Arabia, aiming to improve the development of comprehensive strategies to prevent the transmission of the bacteria among healthcare professionals and consequently among patients and the community.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".