The Potential for Nosocomial Infection Transmission by White Coats Used by Physicians in Nigeria: Implications for Improved Patient-Safety Initiatives
Bibliographic record
Abstract
Microbiological analysis of swabs taken from the cuffs and pocket mouths of physicians' white coats in an acute care hospital showed that 91.3% of the coats had bacterial contamination. Specifically diphtheroids, Staphylococcus aureus and Gram-negative bacilli were isolated. In contrast, comparatively lower rates of bacterial contamination were observed on the white coats (1) of visiting physicians, (2) of the medical unit compared with the rest of the hospital, (3) that were less 1 year old, and (4) that were laundered daily. Further, the white coats of physicians who wore them only when seeing patients had significantly lower bacterial contamination than white coats of physicians who wore theirs during clinical and nonclinical duties (chi(2) = 4.99, df = 1, p < .05). In particular, white-coat cuffs had a higher bacterial load than the mouths of the pockets. The bacterial isolates were resistant to nearly all of the antibiotics tested; the most effective, however, was ciproflox. Results suggest that physicians' white coats may increase nosocomial infection transmission. Proper handling of white coats by physicians and other healthcare workers could minimize cross-contamination and improve patient safety by potentially reducing nosocomial infections.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".