Assessment of infection control in medical surgical units in light of the presence of technology in healthcare facilities
Bibliographic record
Abstract
The use of information technology in clinical settings has brought with it the issue of infection-control that has not been adequately defined, particularly in the Kingdom of Saudi Arabia, where the pace of digital growth has exceeded the accompanying assessment of related microbiological risks. A cross-sectional descriptive survey was conducted to determine adherence to infection -control measures and technological device contamination in the four Ministry of Health hospitals in Saudi Arabia, including 240 health-care workers and 480 environmental swabs at the medical-surgical ward. Structured observation was used, in which WHO hand-hygiene audit instruments were used as well as microbiological screening of the devices, and statistical analysis was performed using ANOVA, multivariate logistic regression, and correlation analysis. Hand-hygiene performance (55.5± 13.9%) and device-cleaning compliance (30.5± 16.0%) were found to be statistically lower among physicians compared to nurses and respiratory therapists (p=0.001). Mobile telephones displayed the highest contamination (85%), followed by an average bacterial count of 56.8± 26.2 CFU. The contamination of the devices that had not been washed for 2 h increased 2.9 times (p < 0.001). Multivariate analysis revealed the device type, spatial location, and frequency of cleaning as independent predictors of contamination (AUC = 0.892). These findings suggest that the rate of technology adoption has exceeded the development of infection-control policies in hospitals in Saudi Arabia, which in turn identifies the key areas of intervention, including physician education, obligatory disinfection of devices, and provision of point-of-care disinfectants.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".